Health benefits of raising ambition in Pakistan’s nationally determined contribution: WHO technical report
Health benefits of raising ambition in Pakistan’s nationally determined contribution: WHO technical report Health benefits of raising ambition in Pakistan’s nationally determined contribution: WHO technical report ISBN 978-92-4-003636-9 (electronic version) ISBN 978-92-4-003637-6 (print version) © World Health Organization 2021 Some rights reserved. This work is available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO licence (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo). Under the terms of this licence, you may copy, redistribute and adapt the work for non-commercial purposes, provided the work is appropriately cited, as indicated below. In any use of this work, there should be no suggestion that WHO endorses any specific organization, products or services. The use of the WHO logo is not permitted. If you adapt the work, then you must license your work under the same or equivalent Creative Commons licence. 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Design and layout: Phoenix Design Aid A/S Acknowledgements v Acronyms and abbreviations vi Abstract vii Background 1 Rationale and scope 2 Global context 3 Co-benefits of fossil fuel subsidy reform and carbon pricing – international examples 3 Aligning energy policy and nationally determined contributions – international examples 4 Research methods 6 Summary of modelling framework and approach 6 GSI-IF methods description 7 Energy demand and emissions modelling with GSI-IF 7 Power generation in the GSI-IF model 9 Air pollution in GSI-IF using GEM 10 CaRBonH methods description 10 Study findings 13 Policy recommendations 22 Lessons learned 25 References 26 Annex 1: Emission factors used for the estimation of air pollutants from energy consumption and power generation 31 Annex 2: Overview of air pollutants from energy consumption and power generation 32 Annex 3: Power generation technologies considered in GEM 34 Contents iii Cover: Wind turbine mills at Jhimpir Pakistan ©Shutterstock.com/Maddy.92 Solar plant, Multan, Punjab, Pakistan ©Shutterstock.com/Rabia Irfan HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORTiv v HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT The World Health Organization in collaboration with the Ministry of National Health Services, Regulations and Coordination, the Ministry of Climate Change and the International Institute for Sustainable Development are grateful to the following members of the technical working group that contributed to this research study. Project coordination led by Tara Neville, WHO Modelling and analytical expertise: • Andrea Bassi, Georg Pallaske (KnowlEdge Srl, International Institute of Sustainable Development) • Joseph V. Spadaro (Spadaro Environmental Research Consultants) Technical and policy expertise: • National consultants: Zafar Fatmi (Aga Khan University), Irfan Yousuf • International Institute for Sustainable Development (IISD): Lourdes Sanchez, Avet Khachatryan, Estan Beedell • Ministry of Climate Change: Syeda Hadika Jamshaid, Saima Shafique, Muhammad Irfan Tarq • Ministry of National Health Services, Regulations and Coordination: Razia Safdar • World Health Organization: Palitha Gunarathna Mahipala, Noureen Aleem Nishtar, Mazen Malkawi, Saleh Rababa, Heather Adair-Rohani, Diarmid Campbell-Lendrum, Pierpaulo Mudu Financial support for this research study has generously been provided by the Wellcome Trust and the International Health Grants Programme of Health Canada. Acknowledgements HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORTvi BAU Business as usual CaRBonH Carbon Reduction Benefits on Health COP Conference of the Parties GHG Greenhouse gas IISD International Institute for Sustainable Development IMF International Monetary Fund NDC Nationally Determined Contribution OECD Organisation for Economic Co-operation and Development UN United Nations UNFCCC United Nations Framework Convention on Climate Change WHO World Health Organization WB World Bank Acronyms and abbreviations vii HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT The following technical report outlines the rationale, process and results of a joint research study, coordinated by the World Health Organization (WHO), co-chaired by the Ministry of National Health Services, Regulations and Coordination and the Ministry of Climate Change, and in collaboration with the International Institute for Sustainable Development (IISD) and leading international and national experts. A rationale section describes the links between fossil fuel burning and adverse health outcomes, drawing on international literature with reference to the Pakistan context. A summary of the research study describes how scenarios were modelled to examine the health and economic implications of raising ambition in Pakistan’s nationally determined contribution (NDC) to the United Nations Framework Convention on Climate Change (UNFCCC). The scenarios simulated were (i) NDC reference case, assuming no change in climate mitigation relative to 2020, (ii) business as usual (BAU), considering the continuation of historical trends resulting in slow but continued progress towards decarbonization, (iii) current ambition, taking into account the current policy ambition outlined in sectoral plans, and (iv) high ambition, assuming an increase in climate mitigation ambition, in alignment with the ongoing discussions related to the update of the NDC. Abstract The results of the study indicate that the high ambition scenario compared to the NDC reference case scenario would result in a reduction of greenhouse gas (GHG) emissions of approximately 27.5% in 2030. These GHG emissions reductions would be accompanied by significant air quality improvements that could prevent more than 65 000 deaths annually from ambient air pollution in 2030. The process outlined in this technical report can serve as a template for future studies that seek to demonstrate how strong, ambitious national climate commitments can result in significant health gains. Zorlu wind power plant at Jhimpir Sindh Pakistan, 2021 ©Shutterstock.com/Aqib Yasin 1 HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT The burning of fossil fuels creates air pollution that leads to noncommunicable diseases, including cardiovascular disease, chronic respiratory conditions, acute lower respiratory infections and certain cancers (Campbell-Lendrum & Prüss-Ustün, 2018). Fossil fuels are also the main contributor to GHGs that cause climate change, which, in turn, is strongly linked to environmental risk factors for noncommunicable diseases and other health impacts (Watts et al., 2021). These negative externalities are not priced into the cost of fossil fuels; society bears the extra cost in the form of higher healthcare expenses and increased mortality and morbidity. Subsidies to fossil fuels further increase the spread between the cost of fossil fuels to the consumer and the cost to society. In doing so, subsidies encourage the overconsumption of fossil fuels beyond the social optimum (SEI et al., 2020). They also place a burden on government finances, with a high opportunity cost for other priorities like public health. Finally, supporting fossil fuels has a lock- in effect, limiting the deployment of clean forms of energy that can significantly reduce air pollution. In the current context driven by COVID-19, it is more important than ever that government energy policies are aligned with health, climate and social priorities. For this reason, the United Nations (UN) Secretary General (United Nations, 2021), the European Union (EU) (Council of the European Union, 2021), the G20 (G20 Research Group, 2009), the World Health Organization (WHO) (World Health Organization, 2020b) and many other organizations are calling for an end to fossil fuel subsidies. At the same time, renewable energies and the electrification of transportation are attracting significant attention in developed and developing countries. The cost of renewable energy technologies has rapidly decreased over the past 20 years and these technologies now dominate most capacity additions (International Energy Agency, 2020; IRENA, 2020). Several countries have made commitments to support and deploy electric vehicles, which can significantly reduce air pollution in cities. However, for a positive health and climate effect, the electrification of transportation or other energy uses mostly relying on fossil fuels (such as heating), requires a clean electricity mix and a strong electricity network. Governments must therefore prioritize this infrastructure. In addition, as COP26 approaches, more countries are pricing emissions. More countries introduced carbon pricing in 2019 than in any previous year, including for the first time in Africa (South Africa) and Asia (Singapore) (World Bank, 2020). Background HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT2 Pakistan has taken important steps to be aligned with these international trends, including ambitious renewable energy and electric vehicle targets (Deccan Herald, 2020; Uddin, 2020). In recent years, Pakistan has made efforts to reform its fossil fuel subsidies for natural gas and other petroleum products (Gould & Adam, 2020). However, there is still an opportunity for further reform to support climate and health targets, as well as to release funds to support other priority areas. The International Monetary Fund (IMF) estimates that in 2017 fossil fuel subsidies in Pakistan were US$ 3.2 billion (IMF, 2018). In addition, it estimates US$ 13.1 billion in costs of climate, health and other externalities caused by the use of fossil fuels, not including foregone government revenue. When the costs of these externalities are accounted for, the benefits of energy policy reforms become more compelling. WHO recommends that health co-benefits, in particular policies that reduce air pollution and short-lived climate pollutants (SLCPs), should be prioritized in NDCs. SLCPs are responsible for a large share of air pollution mortality, as well as having high global warming potential (World Health Organization, 2020a). Some countries have already included health and fossil fuel subsidy reform in their NDCs but these are still limited in numbers and there is room for more focus on the health co-benefits of climate action in country strategies. Although 70% of NDCs submitted as of December 2019 include public health considerations, only 10% (18 out of 184) highlight the health co-benefits of greenhouse gas mitigation policies (World Health Organization, 2020a). More progress is especially needed for commitments to monitor the health co-benefits of climate action and to inform decision making. Of the countries whose NDCs reference the health impacts of climate change, the most cited impacts are vector- borne diseases followed by food and nutrition (World Health Organization, 2020a). This indicates a greater inclusion of the impacts of climate change and a concern for adaptation policies, compared to the mitigation of air pollution. For example, Sri Lanka’s NDC cites an increase in seasonal outbreaks of dengue as a health impact linked to climate change (Ministry of Mahaweli Development and Environment, 2016). It lists 14 health-related activities mainly linked to climate change adaptation, including strengthening vector-borne disease surveillance systems, strengthening the water quality surveillance system, and strengthening the surveillance system for non-communicable diseases (Ministry of Mahaweli Development and Environment, 2016). Some countries have considered these interlinkages and included related targets in their NDCs. For example, Mexico has pledged in its first NDC to take actions to reduce SLCPs, noting the co-benefits to human health and ecosystems (Ministry of Environment and Natural Resource, 2020). Specifically, Mexico has unconditionally committed to reducing black carbon emissions by 51% by 2030 compared to BAU, and conditionally is targeting a 70% reduction of black carbon emissions by 2030 compared to BAU (Ministry of Environment and Natural Resource, 2020). In 2020, Mexico released its national SLCP strategy, outlining concrete actions it is taking to reduce black carbon and methane emissions, including increasing the uptake of electric vehicles and the use of public transport; using cleaner fuels and technologies for cooking; and controlling particulate pollution from sugar mills (CCAC, 2020). NDCs also address direct air pollution while targeting greenhouse gas reduction, resulting in significant health co- benefits. This is the example of China, that also defined in their 13th Five-Year Plan a comprehensive policy package to mitigate climate change and reduce air pollution that is expected to produce substantial health co-benefits (Sanchez & Wang, 2020). China’s package included programmes to expand renewable energy, policies to limit and reduce fossil fuel use, and policies to address air pollution (Sanchez & Wang, 2020). One study estimates that realizing China’s NDC targets will prevent roughly 20 000 premature deaths in 2030, and between 225 000 and 368 000 premature deaths in 2050 compared to BAU (Cai et al., 2018). Rationale and scope 3 HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT As well as being the primary source of carbon dioxide emissions, the combustion of fossil fuels is a major cause of air pollution (Smith et al., 2014). The impacts of these two consequences of fossil fuel use are interlinked; as temperatures increase due to climate change, the health impacts of fine particulate matter become more severe (Silva et al., 2017). Climate change is therefore likely to lead to an increase in mortality from diseases linked to air pollution (Smith et al., 2014). WHO estimates that ambient and household air pollution currently cause 7 million premature deaths annually from cardiovascular disease, respiratory diseases, stroke, chronic obstructive pulmonary disease, and cancer such as lung, trachea and bronchus cancer (World Health Organization, 2014b). Policies to lower GHG emissions typically also lower air pollution, producing co-benefits by reducing both the direct and indirect health impacts of climate change. For this reason, WHO recommends that health co-benefits be identified and included in cost-benefit analyses of climate policies, emphasizing that the value of health co-benefits, like other externalities, may outweigh the implementation costs of the adopted climate policies (World Health Organization, 2020a). India increased taxes on petrol and diesel in March 2020 to help raise funds to support COVID-19 recovery. The hike of 3 rupees per litre was expected to result in up to US$ 5.42 billion in additional annual revenue (Varadhan & Ahmed, 2020). Though implemented for fiscal reasons, the policy will also have an effect in lowering air pollution from fossil fuels. It will complement other policies such as India’s car sales tax, introduced in 2016 to combat high levels of air pollution and congestion (Burke, 2016). Indonesia introduced major reforms to fossil fuel subsidies in 2014, removing subsidies on gasoline and significantly reducing subsidies on diesel. The move, coupled with a drop in oil prices, resulted in US$ 15.6 billion in additional government revenue, with which Indonesia was able to increase funding for ministries to spend on human and economic development programmes, state-owned enterprises to invest in infrastructure, and transfers to regions and villages (Beaton et al., 2016). The reallocation Global context Co-benefits of fossil fuel subsidy reform and carbon pricing – international examples THE GLOBAL SUBSIDIES INITIATIVE-INTEGRATED FISCAL (GSI-IF) MODEL IN PAKISTAN According to previous modelling, eliminating fossil fuel subsidies would reduce Pakistan’s GHG emissions by an estimated 1.43% in 2030 compared to BAU (Gass et al., 2019). Combined with a 10% energy tax, and investing 20% of savings from the fossil fuel subsidies reform towards energy efficiency and renewable energy, Pakistan could achieve a 4.59% reduction in emissions in 2030 (Gass et al., 2019). Although appearing modest, over time an annual reduction of this scale would produce a large impact. Cumulative absolute emissions for the years 2025 to 2030 under an FFSR scenario compared to BAU are estimated at 24 342 kt of CO2e, more than the annual carbon emissions of Sri Lanka (Gass et al., 2019; World Bank Group, 2021b). Fossil fuel subsidies also disadvantage clean solutions while under-pricing fossil fuels and creating risks for stranded assets (Merrill et al., 2015; SEI et al., 2020). With FFSR, as markets adjust to new price signals, progressive investments in clean energy could generate further efficiency gains and have a compounding effect on lowering emissions intensity. HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT4 of Indonesia’s fuel subsidies helped to reduce poverty and improve access to basic needs like water, sanitation and lighting. It also contributed to economic growth, with likely benefits to services, manufacturing, construction, agriculture, transport and communications (Beaton et al., 2016). Aligning energy policy and NDCs – international examples Energy policies and regulations have a strong impact on achieving climate targets. Increasing renewables, investing in energy efficiency, and reflecting the externality cost of fossil fuels through carbon pricing or fossil fuel subsidy reform (FFSR) can lead to significant emissions reductions, resulting in positive climate and health impacts. The Global Subsidies Initiative (GSI) conducted modelling across 26 countries, considering reform of their fossil fuel subsidies, adding a 10% energy tax and recycling a portion of the savings from FFSR into energy efficiency (20%) and renewable energy (10%). This resulted in cumulative GHG reductions of 10.63 Gt of CO2e by 2030, reducing emissions by 13.2% compared to BAU (Figure 1) (Gass et al., 2019). According to the modelling, fossil fuel subsidy phase out alone could generate an average drop of 6% in emissions for the year 2030. This is aligned with global estimates that the removal of all fossil fuel subsidies would lower carbon emissions by between 6.4 and 8.2% by 2050 (Burniaux & Chateau, 2014; Schwanitz et al., 2014). Figure 1 Average percentage of CO2e emissions reductions modelled across 26 countries (SOURCE: GSI, 2019) 0% -2% -4% -6% -8% -10% -12% -14% 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 FFSR renewable energy energy efficiency TAX Remove subsidies to fossil fuels Subsidy savings reallocation period Tax revenue reallocation period Invest 10% of savings in renewable energy Invest 20% of savings in energy efficiency Introduce 10% energy tax Following the Paris Agreement, 14 countries referenced FFSR as a means of meeting their GHG emissions reduction targets in their NDCs (Gass et al., 2019). Of these, 11 were low-income or middle-income countries (Terton et al., 2015). Prominent economies included China and India, while Egypt and the United Arab Emirates made FFSR commitments with the largest potential impact on emissions relative to their baselines (Terton et al., 2015). As NDCs are updated in advance of COP26, countries are being urged to consider adding FFSR as an action to increase their ambition for GHG mitigation (Gass et al., 2019; United Nations, 2021). Of the eight countries that have submitted a second NDC, one, Suriname, has committed to reforming its energy subsidies to promote the consumption of renewable energy (Cabinet of the President of the Republic of Suriname, 2019; UNFCCC, 2021). 5 HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT From 2011 to 2016, Suriname spent an average of 3.3% of GDP on energy subsidies. Its NDC pledges to introduce more cost-reflective electricity tariffs that promote energy efficiency and reduce GHG emissions (Cabinet of the President of the Republic of Suriname, 2019). Savings from the reforms should allow financial room for other initiatives in its NDC, including replacing existing diesel generation with solar photovoltaic (PV), and providing consumer subsidies for the purchase of energy efficient appliances (Cabinet of the President of the Republic of Suriname, 2019). HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT6 Research methods Summary of modelling framework and approach When it comes to determining the impacts of climate change and health, policy decisions and economic, social and natural systems interact together to form a national policy context. These systems are complex, and feedback within them produces non-linear outcomes, making them difficult to predict. Models replicate these systems as best as possible to better forecast the impacts of policy decisions on health, environmental and other outcomes. To conduct estimates of the health impacts of different energy policy scenarios in Pakistan, three models were used (see Figure 2). These followed a staggered approach, with outputs from one model fed into the next to capture the full chain of causality between fossil fuel use and health outcomes. The GSI-IF model was used to estimate the impact of energy policy changes on national GHG emissions. The Green Economy Model (GEM) was used along with the GSI-IF model to estimate the emissions of air pollutants (e.g. PM2.5) from final domestic energy consumption and from power generation. The Carbon Reduction Benefits on Health (CaRBonH) model was then used to estimate the impact of air pollutants on various health outcomes including averted mortality. Finally, the economic benefit of averted mortality was calculated using a Pakistani-specific value of a statistical life (VSL), determined using the Organisation for Economic Co-operation and Development (OECD) benefit transfer methodology (Organisation for Economic Co- operation and Development, 2012). These models were used for their flexibility to adapt to the Pakistan context, the ability to use national-level data as inputs, and because of their use and validation by world-leading organizations in the field. The GSI-IF model has been used to model the effects of fossil fuel subsidy reform on 28 countries1 and its methodology has been validated by Figure 2 Models used in this study Global Subsidies Initiative – Integrated Fiscal (GSI-IF) Model Estimates the impact on national GHG emissions of fossil fuel subsidy reform and carbon tax, as well as the impacts of increased investment on renewable energy and energy efficiency resulting from the savings from subsidy reform and additional funds from fossil fuel taxation. Green Economy Model (GEM) Estimates the emissions of 11 air pollutants from final domestic energy consumption and from power generation. Air pollutants considered in GEM are: CO2; CO; SO2; NO2; NMVOC; CH4; PM10; PM2.5; Black carbon; Organic Carbon; and Ammonia (NH3). CaRBonH Model Quantifies the physical and economic consequences for human health using data on emission reductions of key air pollutants that could be achieved by future climate policies. Air pollutants considered are primary particulate matter (PM10), SO2, NOx and NH3. 7 HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT the International Monetary Fund (IMF) (Gass et al., 2019). The GEM has been customized for Indonesia, Mauritius, Cambodia and Mozambique, among other countries, and follows a framework developed by the United Nations Environment Programme (UNEP) (Bassi, 2015). The CaRBonH model was developed by WHO and has been used to model health effects from air pollutants in countries across the globe (Spadaro et al., 2018). These models together can provide a representation of Pakistan’s dynamics that reflects as closely as possible, using international best practices, the country’s complex reality. Various scenarios can then be tested with different assumptions to forecast the consequences of policy decisions. GSI-IF methods description The GSI-IF model is a systems dynamics model that estimates the impact on national GHG emissions of FFSR and carbon tax, as well as the impacts of increased investment on renewable energy and energy efficiency resulting from the savings from subsidy reform and additional funds from fossil fuel taxation. It was designed to inform national-level reform planning and strengthen international reporting around fossil fuel subsidies, especially regarding NDCs to the Paris Agreement. The GSI-IF model takes national-level data on energy consumption by sector and source as a baseline. Gross domestic product (GDP) growth is currently based on the IMF Economic Outlook and population is based on the UN World Population Prospects database. Subsidy data are drawn from the International Energy Agency (IEA) and IMF. For this study, national data from Pakistan were used, except for energy subsidy data, which were sourced from the IEA. Energy consumption over a given time is then adjusted based on demand elasticities associated with changes to GDP, population and technology (energy efficiency). The price effect is calculated for energy price changes and energy subsidies or taxes. The GSI-IF model also takes into account the substitution effect whereby consumers switch to different forms of energy depending on relative prices. Final energy demand is estimated by source based on the interaction of demand drivers with these effects. Finally, total national GHG emissions are calculated based on the known emission factors of each energy source. Energy demand and emissions modelling with GSI-IF The national energy consumption forecasts generated by the GSI-IF model are disaggregated by fuel source and sector. Specifically, the model includes energy consumption from coal, petroleum products, natural gas, biofuels and waste, and electricity. The consumption of each fuel source is further disaggregated into sectoral energy consumption, specifically residential, commercial, industrial and transport sectors. The main structural assumptions of the model are (see Figure 3): • Final energy consumption is estimated considering (1) indicated demand (including the effect of GDP, population and energy efficiency); (2) the price effect; and (3) the substitution effect. Items (1) and (2) are used to estimate demand for energy services. • The potential for fuel substitution is represented by the ratio of an energy price over the national weighted average energy price. This implies that an energy source will become more attractive if its price increases less than others when subsidies are removed or it is taxed less than others. • It is assumed that price effects require a one-year delay to influence energy consumption. 1 Algeria, Argentina, Australia, Bangladesh, Brazil, China, Egypt, Ethiopia, Germany, Ghana, India, Indonesia, Iran, Iraq, Mexico, Morocco, Myanmar, Nigeria, Pakistan, Russia, Saudi Arabia, South Africa, Sri Lanka, Tunisia, United Arab Emirates (UAE), United States of America (USA), Viet Nam and Zambia. HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT8 The model accounts for several “energy demand” variables considering the effects of price and substitution as indicated in Figure 3. Indicated sectoral energy demand is calculated using an initial value (indexed to a baseline year), multiplying it by relative GDP and relative population (both indexed to the same baseline year and raised to the power of a specific elasticity factor) and dividing it by relative energy efficiency (also indexed to the baseline year). The use of subscripts allows for calculating energy demand for the residential, commercial, industrial, and transport sectors within the same variable. The price effect is then added, simply taking indicated demand and multiplying it by relative energy price (indexed to the baseline year), and raised to the power of a price elasticity. The removal of fossil fuel subsidies and addition of fossil fuel taxes is reflected in energy price changes. When subsidies are removed or taxes added, it is assumed that energy prices will increase for all sectors (unless it is known that subsidies/taxes are allocated to specific users). In the case of Pakistan, subsidy reform was applied evenly across all sectors, but differentiated by fuel type based on the subsidized percentage of the cost of each fuel. The extent to which subsidies are removed and the timeline can be also modelled. In the case of Pakistan, the model assumed full removal of subsidies in the Current ambition and High ambition scenarios by 2025, and a 10% fossil fuel tax implemented between 2025 and 2030 in the High ambition scenario. Subsequently, the substitution effect is considered. The formulation is the same as the one used for incorporating the price effect, but a delay of one year is used to represent the lag existing between price changes and demand (or consumption) changes. The potential for substitution from one energy source to the other, due to price changes (e.g. as a result of fossil fuel subsidy removal), is incorporated by using the ratio of energy source price over the average energy price of the country (estimated as a weighted average of all energy prices). This ratio is also indexed, to ensure consistency with the use of elasticities. Figure 3 GSI-IF model sketch, highlighting the main steps considered for estimating CO2e emissions reductions resulting from fossil fuel subsidy reform and taxation Indicated demand Price effect Substitution effect Final demand (consumption) CO2e emissions Income elasticity Population elasticity Price elasticity Price elasticity Initial energy demand GDP Population Technology (energy efficiency) Energy price Subsidies and taxation Subsidy savings and tax revenues Price ratio: energy source price over average energy price Indicated energy demand (with price effect) Share of energy demand by source (with subsitution effect) Emission factors (by energy source) Renewable energy Price effect Resource reallocation Energy efficiency 9 HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT Indicated energy demand (including the price effect) is used to estimate the total energy that has to be guaranteed at the country level. The potential for substitution is used to estimate the actual share of energy consumption by source. As a result, a normalization is performed, multiplying total indicated energy demand by the shares obtained from the inclusion of the substitution effect. GHG emissions from energy consumption are calculated by sector and fuel using 2006 Intergovernmental Panel on Climate Change (IPCC) emission factors (IPCC, 2007). The normalized sectoral energy demand, by fuel, is multiplied by the respective emission factor. Emissions per TJ of electricity used are calculated with a multiplier based on the shares of oil, coal and gas in the total electricity generation mix, and each source’s emission factors. Nuclear and renewable energy are assumed to have zero carbon intensity per TJ generated. The recycling and repurposing of subsidy savings and tax revenues is done endogenously, by estimating the additional energy efficiency potential and additional renewable energy capacity based on annual fossil fuel subsidy savings or fossil fuel tax revenue. The GSI-IF model allows for reallocating a specific percentage of subsidy savings or tax revenues to energy efficiency and/or renewable energy, and impacts are reflected in altered behaviour for the stock of energy efficiency and the GHG intensity per TJ of electricity. Data required for estimating the impact of revenue recycling are the cost of energy efficiency (per TJ of energy saved) and the cost of renewable generation capacity in Pakistan, estimated based on the average wind and solar power capacity cost from the Energy Information Administration (EIA) and IEA. While additional savings from investments in energy efficiency accumulate into the stock of energy efficiency, the additional renewable energy accumulates in a separate stock, depending on the share of savings reallocated to new renewable capacity. An average load factor is used to determine the electricity generated by this additional renewable capacity. The feedback of additional renewable capacity is introduced through the share of electricity from renewable energy investment. The share of electricity from renewable energy investment feeds back, with a one-year fixed delay, into the equation for the share of oil and coal. In other words, if the reallocation policy is active, the increased share of renewables is assumed to replace coal-based and oil-based power generation. Power generation in the GSI-IF model The power generation module captures the demand for electricity, transmission losses, and required and current power generation capacity. The module uses the total normalized electricity demand as input to assess electricity generation by technology and forecast future capacity requirements. It further provides information about total electricity generation, both by technology and system-wide, and the shares of generation by technology.2 Power generation capacity is represented using two stocks: power generation capacity under construction and power generation capacity. The installation and usage of power generation capacity is driven by the desired electricity generation rate, which considers both imports and transmission losses. The desired electricity generation by technology is then calculated by multiplying the total desired generation by the shares of generation satisfied by the respective technology. Considering the load factor and the number of hours per year, the desired electricity generation by technology is used to calculate the required electricity generation capacity for each technology. The calculation of the power generation capacity gap compares the required power generation capacity with the currently installed capacity. The required electricity generation capacity is compared to the current power generation capacity and power generation capacity under construction to determine whether there is a capacity gap. A MAX function is used to ensure that the capacity gap does not take negative values in case of overcapacity, as a decrease in capacity is assumed to happen through decommissioning only. 2 The power generation module estimates capacity, generation and cost for the 13 technologies presented in Annex 3. HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT10 3 https://leap.sei.org/default.asp?action=IBC 4 CaRBonH (WHO EURO) model, available at http://www.euro.who.int/en/health-topics/environment-and-health/Climate-change/ publications/2018/achieving-health-benefits-from-carbon-reductions-manual-for-CaRBonH-calculation-tool-2018. The stock of power generation capacity under construction is changed by the construction rate and the completion rate. The construction rate is increasing the stock level and calculated by dividing the power generation capacity gap by the time to process capacity orders. The completion rate is an outflow of power generation capacity under construction and an inflow to power generation capacity. The completion rate is defined as a fixed order delay based on the construction rate and the construction time for all technologies, based on the assumption that capacity becomes functional once the construction is completed. Power generation capacity is increased by the completion rate, and decreased by decommissioning, whereby decommissioning is calculated as a fixed delay of the completion rate and the capacity lifetime, based on the assumption that capacity depreciates after a fixed lifetime. Electricity generation is calculated based on the power generation capacity, the load factor, and the hours per year. It represents the total amount of electricity produced. A MIN function is used to ensure that only the required amount of electricity is produced, to avoid overproduction. It compares the current generation potential, by technology, with the desired electricity generation, by technology. If the potential generation is higher, the MIN function ensures the technology in question does not produce more electricity than demanded. The total electricity generation rate represents the sum of electricity generation from all types of capacity and is calculated by using a SUM function to add up the electricity production of all technology subscripts. The sum of the electricity that is produced by hydropower and other renewable capacity types divided by the total electricity generation represents the share that renewable capacity has in total electricity production. The power generation module used an average of wind and solar power capacity cost from the EIA and IEA, considering the full cost of installation. Air pollution in GSI-IF using GEM GEM estimates the emission of air pollutants from final domestic energy consumption and from power generation. In total, GEM estimates 11 pollutants across energy sources and sectors. The emissions factors were obtained from the emissions factor database of the Low Emissions Analysis Platform (LEAP) Integrated Benefits Calculator (IBC), which is publicly available.3 Air pollutants considered in GEM are: CO2; CO; SO2; NOx; NMVOC; CH4; PM10; PM2.5; black carbon; organic carbon; and ammonia (NH3). Air pollutants from energy consumption and power generation are calculated separately. Air pollutants from final energy consumption are calculated by multiplying total final energy demand (by fuel source) by a respective emission factor by type of fuel and sector. Air pollutants from power generation are calculated by multiplying the fuel used for the generation of electricity by a respective emissions per TJ of fuel used multiplier. The emission factors used for the estimation of air pollutants from energy consumption and power generation are summarized in Annex 1. CaRBonH methods description The CaRBonH4 model is a decision-support tool for government planners that uses data on emission reductions of key air pollutants to quantify the health and economic co-benefits that could be achieved by future climate policies. The model quantifies the physical health benefits in terms of reduced incidences of morbidity and mortality, and the aggregated economic gains considering benefits to individuals, and more broadly to national health care budgets and productivity output, as well as intangible societal benefits from gains in quality of life. Pregnancy loss as a result of maternal exposure to high levels of particulate matter was identified as a national consideration for investigation in this study. The CaRBonH model was adapted to include preliminary analysis of pregnancy loss associated with exposure to ambient particulate matter (PM2.5) based on recent epidemiological evidence (Xue et al., 2021). While further investigation is required, preliminary results of this analysis have been included in these study findings. 11 HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT The health impact assessment in CaRBonH follows an impact pathway analysis. The approach explicitly traces the fate of pollutants from the moment they are released into the environment, followed by atmospheric dispersion, and removal by pollutant deposition and chemical transformation. Vulnerable population subgroups, such as sick people, children, and older people, who are exposed to atmospheric contaminants through inhalation and/or ingestion pathways are at a higher health risk of suffering from adverse morbidity symptoms. These range from mild discomfort to more serious life-threatening conditions requiring medical attention to premature mortality. Health burdens are calculated using exposure-disease outcome risk functions. Lastly, physical burdens are monetized considering health care expenditures, costs to the individual, economy productivity losses, and the impact of illness on quality of life due to pain and suffering (intangible costs). Future costs are discounted, with typical discount rates varying between 3% and 7%, and are uplifted to account for real income growth changes (values are country dependent) to yield present values. Except for cancer risks, usually, it’s assumed there is no time delay (cessation lag) between exposure and outcome. The CaRBonH modelling framework is illustrated in Figure 4. The minimalistic core user inputs to the software consist of the reduced emissions of PM, SO2, NOx and NH3, or any combination of these pollutants, due to changes related to reductions in national GHG budgets consistent with the 2030 proposed NDC pledge (dark blue box in Figure 4). Country-level data to apply the CaRBonH model to Pakistan came from the World Bank (2021), the Institute for Health Metrics and Evaluation (IHME) (2021), the Health Effects Institute (2020), and the United Nations – World Population Prospects (UN-WPP) (2019). Figure 4 CaRBonH modelling framework Calculation steps Define scenario targets Calculate concentration and exposure changes Evaluate health risk change Calculate economic co-benefit of scenario Projected emission of major pollutants Decision analysis Inform policy maker & stakeholders CEA & BCA (market vs. benefit trade-off) Feedback (increase target ambitiousness) Major air pollutant emission reductions expected from GHG interventions in NDCs (total, or sector-specific values to be supplied by countries according to national action plans) Source-receptor matrices (calculate changes in downstream ambient air concentrations associated with reductions in national emissions of PM, NO2, SO2 and NH3) Country-level & regional-level changes in air pollution Population at risk Health effects Economic assessment Health co-benefits of GHG reductions Population- weighted exposure Concentration response functions mortality & morbidity Health statistics background mortality and morbidity rates HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT12 Reduced GHG emissions could be achieved through improvements in energy efficiency; fuel quality standards; shifts to less polluting technologies in power generation or mobility sectors; innovations in industrial manufacturing processes; interventions that target emissions reductions from buildings; actions related to land use and land-use changes and forestry (LULUCF); and encouraging (so-called nudging) environmentally friendly consumer behaviour and government action (e.g. eating less meat, imposing monetary disincentives or taxes). Regarding fossil fuel combustion processes, pollutant reductions from GHG control measures are generally linearly correlated to decreases in carbon emissions. For regions with a large carbon reduction potential from non-fossil fuel sources (e.g. LULUCF), the co-pollutant relationship is non-trivial. Source-receptor (SR) or transfer matrices are used to link changes in emissions into improvements in ambient air quality within the “emitter” country and a downstream location or “receiver country” from reduced transboundary pollutant transport (orange box in Figure 4). In this study, only the health effects across Pakistan (the emitter country) have been calculated. Next, the concentration changes are multiplied by the population at risk for the relevant health outcome, for example children at risk of an asthma attack (dark yellow boxes in Figure 4), to calculate the population-weighted exposure at the country level (leftmost grey box in Figure 4). The health impact assessment combines population exposure with data on illness (morbidity) prevalence and baseline mortality rates (rightmost grey box in Figure 4) and concentration- response functions for morbidity and mortality (central grey box in Figure 4) to calculate the fine particulate matter (PM2.5) associated health effects (brown box in Figure 4). The contribution to PM2.5 from NO2, SO2 and NH3 emissions comes from the production of secondary aerosols; these are the products of chemical reactions in which these precursor pollutants combine with other species present in the atmosphere to produce nitrate and sulphate aerosols. Regarding mortality, premature deaths are calculated using long-term (longer than one year) concentration-response functions. Various morbidity outcomes are considered, including hospital admissions due to cardiopulmonary illnesses, lost workdays (absenteeism), respiratory symptoms in children and adults, and restricted activity (disability) days when a person’s ability to engage in daily routines is disrupted (e.g. having to stay at home from work or school due to minor illness symptoms, or having to go to the doctor’s office or hospital emergency department to seek medical treatment in more severe cases). Finally, the health co-benefits (averted premature deaths and cases of illness) are multiplied by the cost per incidence (e.g. cost per case of asthma in children, or welfare cost per postponed premature death) to calculate the economic co-benefit achieved through national carbon reductions (light blue box in Figure 4). Benefit costs account for avoided expenditures to individuals and the health care system, economic gains in labour productivity, and intangible benefits from social welfare gains associated with positive changes in quality of life from reduced pain and suffering across the affected population. 13 HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT For the analysis of energy policy reforms on energy demand and GHG emissions in Pakistan, the GSI-IF model was used to simulate four scenarios: (i) NDC reference case, (ii) BAU (iii) current ambition, and (iv) high ambition. These correspond to the following assumptions (see Table 1 for details on each scenario): • The NDC reference case represents a scenario with a fixed baseline in 2020. This is a hypothetical scenario where no action towards decarbonization is assumed, neither from policy nor from investments. It does not consider any investment or policy action already happening in the country so that it can be used as the base of inaction. • BAU represents a scenario in which investments in low-carbon development will continue as in the past (following the historical trend) but there is no additional policy to increase ambition (i.e. investments are considered, but no new policies to stimulate further investments are taken into account). For instance, this scenario assumes that if a vehicle reaches its end of life, it will be replaced with a newer, more efficient one. This newer vehicle will use the same technology (e.g. internal combustion) but will use fuel more efficiently and will generate fewer emissions than the vehicle it replaces, according to the technology state of the art. It is different from the NDC reference case in the sense that it assumes vehicles are maintained and their lifetime is continuously extended, implying efficiency does not improve over time and the same amount of emissions will be generated from this and other vehicles. • The current ambition scenario includes recent policy announcements, including a pledge to dramatically scale up renewable energy generation capacity and a target of 30% new sales of electric vehicles by 2030 (Iqbal, 2020). The scenario considers that investment continues its historical trend. • The high ambition scenario is a hypothetical scenario that assumes higher ambitions concerning renewable energy, energy efficiency, transport electrification, fuel grade, fossil fuel subsidy reform, and the introduction of fuel taxes. This scenario implies investment continues its historical trend but policy ambition is higher than the policies/targets currently approved. Scenario assumptions for the four scenarios simulated are presented in Table 1. The energy demand projections generated by the model are presented in Figure 5. In the NDC reference case, total country energy demand is forecasted to increase from 3.51 million TJ per year in 2020 to 4.69 million TJ per year in 2030, and 6.31 million TJ per year in 2050. In the alternative scenarios, the impacts of energy efficiency, renewable energy, fuel substitution and fossil fuel subsidy reform cause energy demand to change, compared to the NDC reference case. In the current ambition scenario, energy demand in the years 2030 and 2050 is projected at 4.16 million TJ per year and 5.35 million TJ per year respectively, which is 11.4% and 31.1% lower compared to the NDC Reference Case during the same years. In the high ambition scenario, total energy demand peaks around 2030 at an annual demand of 3.77 million TJ. Between 2030 and 2050, total energy demand is projected to decline from 3.77 million TJ in 2030 to 3.41 million TJ in 2050, due to increases in energy efficiency. The projected energy demand in the high ambition scenario is 19.6% and 46.1% lower compared to the NDC reference case in 2030 and 2050 respectively. For each scenario, the model estimates shares of energy demand by sector. The largest changes are driven by the electrification of transport and residences. By 2030, in the high ambition scenario, electrification contributes to a slight reduction (-0.74%) in the transport share in total energy demand compared to the NDC reference case. The residential sector shows a slight increase of 0.67% in the high ambition scenario compared to the NDC reference case, with GDP Study findings HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT14 Table 1 Summary of scenario assumptions Intervention NDC reference case BAU Current ambition High ambition Renewable energy Same as today 55% by 2030 65% by 2030 75% by 2030 Energy efficiency No change +0.5%/year +1.5%/year +3%/year Share of new electric vehicle sales No change 2030: 10% 2040: 10% 2030: 30% 2040: 90% 2030: 40% 2040: 100% Fuel switching from biomass to electricity (households) No change 2.5% by 2030, 7.5% by 2050 5% by 2030, 15% by 2050 10% by 2030, 30% by 2050 Fuel grade EURO2 EURO5 EURO5, upgrade vehicle fleet to EURO6 by 2030 Fossil fuel subsidy phase out N/A 100% by 2025 Fossil fuel tax N/A 10%, implemented between 2025 and 2030 Figure 5 Total energy demand 7,0 6,5 6,0 5,5 5,0 4,5 4,0 3,5 2,5 2,0 20 08 20 10 20 12 20 14 20 16 20 18 20 20 20 22 20 24 20 26 20 28 20 30 20 32 20 34 20 36 20 38 20 40 20 42 20 44 20 46 20 48 20 50 t m / y ea r NDC reference BAU Current ambition (FFSR) High ambition (FFSR + tax) Time (Year) growth contributing to greater household energy consumption. These trends also influence the shares of energy demand by fuel type. The electrification of the transport sector and fossil fuel subsidy reform increase the demand for electricity (+2.5%) and lead to a decline in the shares of petroleum (-0.9%) and natural gas (-1.7%) in total energy demand compared to the 2030 NDC reference case projections. Increased domestic electrification leads to a further reduction in total biomass demand in the high ambition scenario (-1.1% vs 2030 BAU). Baseline energy intensity per million Pakistani Rupees (Rs) in real GDP is projected to decline from 18.5 TJ per million 15 HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT Rs in 2020 to 15.0 TJ per million Rs generated between 2020 and 2050, a decline of 18.9% (see Table 2). In the current ambition and high ambition scenarios, energy intensity is projected to decline by 11.4% and 19.6% by 2030 respectively compared to the NDC reference case. Continued implementation contributes to further reductions in energy intensity by 2050. In 2050, energy intensity per million Rs generated is projected at 10.4 TJ per million Rs in the current ambition scenario (-31.1% vs NDC reference case) and 8.1 TJ per million Rs in the high ambition scenario (-46.1% vs NDC reference case). The model assesses the impacts of energy efficiency and FFSR separately. The results for the impacts of energy efficiency and the reforming of fossil fuel subsidies in the current ambition and high ambition scenarios are presented in Figure 6. In the current ambition scenario, total energy demand in 2030 is 11.4% lower compared to the NDC reference case. energy efficiency (and electrification) and the phase out of fossil fuel subsidies contribute to reductions of 8.7% and 2.7% respectively. In the high ambition scenario, by 2030 total energy demand is 19.6% lower compared to the NDC reference case. This is composed of a 15.9% reduction from energy efficiency and electrification, 2.5% from the phase out of subsidies, and 1.2% from fossil fuel tax. Table 2 Energy intensity per million Pakistani rupees (TJ/Rs m) Scenario Unit 2020 2025 2030 2040 2050 NDC reference case TJ/Rs m 18.5 18.0 17.4 16.3 15.0 BAU TJ/Rs m 18.5 17.7 16.9 15.2 13.6 % vs NDC reference case % 0.0% -1.4% -2.9% -6.4% -9.6% Current ambition TJ/Rs m 18.5 16.8 15.4 12.7 10.4 % vs NDC reference case % 0.0% -6.6% -11.4% -22.0% -31.1% High ambition TJ/Rs m 18.5 16.1 14.0 10.6 8.1 % vs NDC reference case % 0.0% -10.6% -19.6% -34.6% -46.1% Figure 6 FFSR impacts on total energy demand – Current ambition and High ambition scenarios 0% -10% -20% -30% -40% -50% 0% -10% -20% -30% -40% -50% Reduction from EE Reduction from FFSR Reduction from tax Current ambition High ambition 2020 2025 2030 2040 2050 2020 2025 2030 2040 2050 -4,5% -8,7% -8,5% -15,9% -29,8% -40,5% -18,4% -26,6% -2,1% -2,7% -2,1% -0,0% -2,5% -1,2% -3,2% -3,8% -1,8%-1,6% -3,7% -4,9% HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT16 The projected total CO2e emissions for all scenarios are presented in Figure 7. In the NDC reference case, total CO2e emissions from energy are forecasted to increase from 303.8 million tonnes in 2020 to 398.3 million tonnes in 2030 and 528.9 million tonnes in 2050. In other scenarios, the increased share in renewables compared to the NDC reference case contributes to the reduction in total emissions together with efficiency improvements and fuel switching. In the current ambition scenario, annual emissions in 2030 and 2050 total 329.6 million tonnes and 334.4 million tonnes respectively. Annual reductions in emissions total 68.7 million tonnes by 2030 and increase to 194.5 million tonnes in 2050. In the high ambition scenario, annual CO2e emissions are projected to decline to 288.9 million tonnes in 2030 (-27.5% vs NDC reference case) and 236 million tonnes in 2050 (-55.4% vs NDC reference case). Overall, sectoral shares of emissions by sector in the scenarios modelled follow a similar pattern to energy demand by sector, with one notable difference: in the high ambition scenario, the sectoral contribution to total emissions for transport follows the reverse trajectory forecasted for the composition of energy demand. While the transport sectoral share in total energy demand forecasted for the high ambition scenario in 2030 is lower compared to the NDC reference case (-0.8%), the sectoral contribution to total emissions increases by 1.2%. The largest change in relative emissions is from the residential sector. As households electrify, the residential share in total emissions declines between 2020 and 2030 in both the NDC reference case (-5.4%) and the high ambition scenario (-5.2%). For shares of emissions by fuel, the most important difference between the NDC reference case and the high ambition scenario is the smaller share of emissions from electricity in the high ambition scenario. With more renewable energy, the contribution of electricity to total emissions is 6% lower in the high ambition scenario, despite accounting for a 2.5% higher share of total energy demand. Table 3 provides an overview of the cumulative total emissions avoided in the policy scenarios. The cumulative difference in emissions illustrates the total amount of GHG emissions avoided from the implementation of low-carbon development ambitions – energy efficiency, fuel switching and additional renewable energy, the phase out of fossil fuel subsidies, and the introduction of a fossil fuel tax – in the high ambition scenario. By 2030, the envisaged interventions contribute to total avoided emissions of 148.9 million tonnes (BAU), 360.6 million tonnes (current ambition) and 567.5 million tonnes (high ambition) relative to the NDC reference case. Figure 7 Total CO2e emissions from energy consumption and production 600 500 400 300 200 100 0 20 08 20 10 20 12 20 14 20 16 20 18 20 20 20 22 20 24 20 26 20 28 20 30 20 32 20 34 20 36 20 38 20 40 20 42 20 44 20 46 20 48 20 50 t m / y ea r NDC reference BAU Current ambition (FFSR) High ambition (FFSR + tax) Time (Year) 17 HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT Differing rates of carbon intensity per TJ of energy consumed mean GHG reductions are greater than changes to energy demand in the modelled scenarios relative to the NDC reference case. NDC reference case carbon intensity is forecasted to remain almost constant, at around 84.3 tonnes per TJ, between 2020 and 2030 and decline slightly, to 82.8 tonnes per TJ, by 2050. By 2050, forecasts for the current and high ambition scenarios indicate carbon intensities of 70.9 tonnes per TJ (-14.4% vs NDC reference case) and 58 tonnes per TJ (-30% vs NDC reference case) respectively. The GEM model forecasts air pollutants from the final consumption of energy and from fuel use for power generation. The projections indicate a reduction of air pollutants across all scenarios compared to the NDC reference case. Reductions in air pollutants are achieved by reducing fossil fuel use for power generation and through fuel switching from fuels with high pollutant intensity such as biomass to cleaner sources such as electricity, as well as through energy efficiency. The forecasted development of total PM10, NOx and SO2 emissions in all scenarios are presented in Figure 8, Figure 9 and Figure 10 respectively. An overview of all pollutants estimated by the GSI-IF model for the years 2020, 2030 and 2050 is presented in Annex 2. Table 3 Cumulative avoided emissions compared to the NDC reference case, selected years Scenario Unit 2025 2030 2040 2050 BAU t m CO2e 35.5 148.9 572.8 1231.4 Current ambition t m CO2e 93.2 360.6 1392.2 3073.7 High ambition t m CO2e 144.2 567.5 2193.7 4754.7 Figure 8 Total PM10 emissions 1,2 1,0 0,8 0,6 0,4 0,2 0,0 20 08 20 10 20 12 20 14 20 16 20 18 20 20 20 22 20 24 20 26 20 28 20 30 20 32 20 34 20 36 20 38 20 40 20 42 20 44 20 46 20 48 20 50 t m / y ea r NDC reference BAU Current ambition (FFSR) High ambition (FFSR + tax) Time (Year) HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT18 Figure 9 Total NOx emissions 2,0 1,5 1,0 0,5 0,0 20 08 20 10 20 12 20 14 20 16 20 18 20 20 20 22 20 24 20 26 20 28 20 30 20 32 20 34 20 36 20 38 20 40 20 42 20 44 20 46 20 48 20 50 t m / y ea r NDC reference BAU Current ambition (FFSR) High ambition (FFSR + tax) Time (Year) Figure 10 Total SO2 emissions 4,0 3,5 3,0 2,5 2,0 1,5 1,0 0,5 0,0 20 08 20 10 20 12 20 14 20 16 20 18 20 20 20 22 20 24 20 26 20 28 20 30 20 32 20 34 20 36 20 38 20 40 20 42 20 44 20 46 20 48 20 50 t m / y ea r NDC reference BAU Current ambition (FFSR) High ambition (FFSR + tax) Time (Year) 19 HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT The results of the health and economic impact analysis are presented in Table 4. For the high ambition pathway, the averted mortality in 2030 among infants (younger than one year old) and adults older than 25 years old is 65 400 deaths, plus 9 100 averted incidences of pregnancy loss. The averted infant and adult mortality represent a 12% (or one in eight) reduction in the expected number of premature deaths in 2030 under the NDC reference case, while the averted pregnancy loss represents a 3% reduction. As noted above, in this study, estimated pregnancy loss is a preliminary analysis based on recent investigation into the potential impact of ambient air exposure on maternal health. In terms of disability adjusted life years (DALYs) – the sum of years of life gained and years lived without disability (thousands) – there is a gain of more than 3.1 million years. No specific morbidity benefits (e.g. changes in hospital admissions or lost workdays) were calculated due to a lack of reliable baseline data. The total economic benefit is nearly US$ 11 billion (2019 nominal prices) or about 1.8% of the projected GDP of Pakistan in 2030. In comparison to the expected benefits under Pakistan’s current strategy scenario, the high ambition case would deliver a 75% increase in the health co-benefit. It is worth noting that these are national benefits from improvement in PM2.5 ambient air pollution that consider the population exposed across Pakistan, while additional regional benefits would be realised because of the reduced cross-border transport of pollutants into neighbouring countries. Table 4 Health and economic co-benefits of alternative mitigation scenarios compared to the NDC reference case scenario for Pakistan in 2030 Scenario Avoided premature deaths Economic benefits Totals Adults (GEMM) Infants Pregnancy loss Adults Infants Pregnancy loss Deaths DALYs ('000) Valuation ($ m) BAU IQR 9900 6300−12 500 6100 2800−7900 2300 900−2900 1130 520−1490 1090 340−1410 410 110−520 18 300 10 000−23 300 777 382−997 2630 980−3420 Current ambition IQR 23 400 14 700−29 400 13 900 6400−18 000 5200 2100−6700 2660 1210−3500 2470 790−3220 950 260−1200 42 500 23 200−54 100 1792 886−2305 6100 2270−7940 High ambition IQR 41 700 26 100−52 300 23 700 11 100−30 900 9100 3700−11 700 4740 2150−6230 4220 1380−5520 1650 460−2090 74 500 40 900−95 000 3110 1553−4005 10 650 4000−13 890 (Notes: IQR = interquartile range. DALY = disability adjusted life years, the sum of years of life gained and years lived without disability (thousands) due to reduced PM2.5 ambient air concentrations. Economic benefits expressed in millions of USD, 2019 nominal prices, for a 6% discount rate. Numbers may not add up due to rounding.) Figure 11 and Figure 12 summarise the results of this analysis in 2030 by scenario (current, BAU and high ambition), by population subgroup (infants and adults), and for the pregnancy-loss outcome. The box and whisker plots show Importantly, an increase in ambition from the current ambition to the higher ambition scenario results in almost a doubling of lives saved In summary, the results of the study indicate that the high ambition scenario compared to the NDC reference case scenario would result in a reduction of GHG emissions of approximately 27.5% in 2030. These GHG emissions reductions would be accompanied by significant air quality improvements that could prevent more than 65 000 deaths annually from ambient air pollution in 2030. HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT20 Figure 11 Health co-benefits of carbon reductions in Pakistan, 2030 60,000 50,000 40,000 30,000 20,000 10,000 0 300 250 200 150 100 50 0 200,000 80,000 60,000 40,000 20,000 0 25,000 20,000 15,000 10,000 5,000 0 100,000 90,000 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0 N um be r o f d ea th s C ou nt N um be r o f d ea th s N um be r o f d ea th s Adults Infants Pregnancy loss 2.5 10 20 30 40 50 60 70 80 Adults Infants Pregnancy loss Adults Infants Pregnancy loss Current ambition Current ambition BAU High ambition Total: 42,500 averted premature deaths Total: 18,300 deaths Deaths Total: 74,500 deaths Vertical lines indicate 95%CI PM2.5 Reduction 2.01 (μg/m3) PM2.5 Reduction 8.23 (μg/m3) SO2 20% NOx 14% PM2.5 66% PM2.5 72% SO2 19% NOx 9% ≤ 9 00 0 (9 0 00 , 1 4 00 0] (1 4 00 0, 1 9 00 0] (1 9 00 0, 2 4 00 0] (2 4 00 0, 2 9 00 0] (2 9 00 0, 3 4 00 0] (3 4 00 0, 3 9 00 0] (3 9 00 0, 4 4 00 0] (4 4 00 0, 4 9 00 0] (4 9 00 0, 5 4 00 0] (5 4 00 0, 5 9 00 0] (5 9 00 0, 6 4 00 0] (6 4 00 0, 6 9 00 0] (6 9 00 0, 7 4 00 0] (7 4 00 0, 7 9 00 0] (7 9 00 0, 8 4 00 0] (8 4 00 0, 8 9 00 0] (8 9 00 0, 9 4 00 0] (9 4 00 0, 9 9 00 0] Mean = 42,501 deaths Median = 35,790 deaths IQR: 23,244−54,133 deaths 95% CI 68%CI IQR Cumulative % 23,412 13,850 Vertical lines indicate 95%CI Vertical lines indicate 95%CI 5,239 9,942 41,713 6,070 23,690 2,266 9,108 the mean (×), median, IQR and 95% confidence interval of the estimates. The contribution to the total health benefit by precursor pollutant (PM2.5, NOx and SO2) is illustrated by the pie charts for the BAU and high ambition trajectories (Figure 11). The histograms show the uncertainty distribution of the total preventable premature mortality and the total economic benefit of Pakistan’s current strategy scenario. For this study, a Monte Carlo simulation was carried out, considering the uncertainty ranges in each of the steps of the impact pathway analysis: reduction in pollutant 21 HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT Figure 12 Economic benefits of carbon reductions in Pakistan, 2030 10,000 9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0 400 350 300 250 200 150 100 50 0 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1,000 500 0 18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0 M ill io ns $ 20 19 n om in al p ric es (6 .0 % d isc ou nt ed ) M ill io ns $ 20 19 n om in al p ric es (6 .0 % d isc ou nt ed ) M ill io ns $ 20 19 n om in al p ric es (6 .0 % d isc ou nt ed ) C ou nt Current ambition Current ambition BAU High ambition Total: $2.6 billion Total: $6.1 billion Total: $10.7 billion Vertical lines indicate 95%CI Vertical lines indicate 95%CI Vertical lines indicate 95%CI ≤ 6 00 ( 6 00 , 1 5 00 ] (1 5 00 , 2 4 00 ] (2 4 00 , 3 3 00 ] (3 3 00 , 4 2 00 ] (4 2 00 , 5 1 00 ] (5 1 00 , 6 0 00 ] (6 0 00 , 6 9 00 ] (6 9 00 , 7 8 00 ] (7 8 00 , 8 7 00 ] (8 7 00 , 9 6 00 ] (9 6 00 , 1 0 50 0] (1 0 50 0, 1 1 40 0] (1 1 40 0, 1 2 30 0] (1 2 30 0, 1 3 20 0] (1 3 20 0, 1 4 10 0] (1 4 10 0, 1 5 00 0] (1 5 00 0, 1 5 90 0] (1 5 90 0, 1 6 80 0] (1 6 80 0, 1 7 70 0] (1 7 70 0, 1 8 60 0] 18 6 00 Mean = $6,100 million Median = $4,320 million IQR: $2,280−7,940 million (2019 nominal, 6.0% discounted) Adults Infants Pregnancy loss Adults Infants Pregnancy loss Adults Infants Pregnancy loss 2,658 2,474 952 1,127 1,086 412 4,743 4,219 1,653 emissions, change in PM2.5 ambient air concentration, baseline mortality data, relative risk association, and mortality valuation (VSL). In the top right panel insert in Figure 11, the projected mortality (ordinate axis) as a function of the cumulative probability distribution (abscissa axis) is presented, along with the identification of the interquartile range, and the 68% and 95% confidence intervals of the averted premature deaths. HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT22 The significant health benefits and associated health savings in the scenarios modelled provide a strong argument and investment case for setting ambitious climate targets in Pakistan’s NDC. By implementing the following seven energy policy interventions, the Government of Pakistan could achieve ambitious GHG emissions reductions of 27.5% in 2030 (eliminating up to 568Mt of GHGs cumulatively) while preventing more than 65,000 annual deaths in 2030 from air pollution. 1. Increase renewable energy generation up to 75% by 2030, including both hydro and non-hydro sources, above the current aim of 65%. Increasing the share of variable renewable energy sources such as wind and solar at large scale and at the levels proposed in the scenarios requires a preliminary feasibility analysis to understand the capacity of the grid to absorb large percentages of variable renewable energy. This increase would likely result in necessary investments to improve and upgrade Pakistan’s electricity grid. Furthermore, high variable renewable energy integration could be optimized, requiring planning for system integration and working to match energy supply and demand through policies, investments, and international exchange of knowledge and best practices – which would result in higher costs. Decentralized renewable energy generation coupled with power storage offers a more viable option, especially for rural and grid-isolated areas. Overall, the best options for achieving the renewable energy targets should be evaluated. 2. Increase energy efficiency with combined sectoral targets to achieve a total of 3% annual improvement in energy efficiency above the current aim of 1.5%. Pakistan’s development strategies and action plans already prioritize energy efficiency measures. Achieving energy efficiency targets will require inter-institutional and intra-institutional cooperation, strengthening energy data collection and analysis, and investment in replacing obsolete technologies (National Energy Efficiency and Conservation Authority, 2020). In addition, the National Energy Efficiency and Conservation Authority (Nenergy efficiencyCA)’s Strategic Plan (2020–2025) identifies opportunities for innovative non-budget mechanisms to finance energy retrofits; to make use of this opportunity, additional research, capacity building and support for energy service companies (ESCOs) will be needed. 3. Transition from biomass to electricity in 30% of households by 2050, above the current aim of 15%. Biomass fuel is currently being used by more than 50% of households in Pakistan, and according to WHO’s recent estimates, is responsible for nearly 40% of all deaths from respiratory diseases, cardiovascular diseases and cancers among adults, and child deaths related to acute lower respiratory infections. Women and children are at particularly high risk. Electrification of energy use would reduce this health burden by improving household air quality and can reduce GHG emissions and short-lived climate pollutants. Attention is required though to avoid the risk of increasing outdoor air pollution if the electricity generation mix is based on polluting fossil fuels. The transition pathway may, in some circumstances, prioritize liquefied petroleum gas (LPG) for clean cooking and solar lamps or solar-based mini-grids to ensure reliable, safe and healthy access to energy for lower-income households. 4. Increase the share of electric vehicles up to 100% of all new vehicles by 2040; the current aim is 90% by 2040. To reach current targets, Pakistan is introducing goods and services tax (GST) reductions and lower electricity tariffs for electric vehicles, import duty reductions on charging equipment, and various manufacturing incentives. Electric vehicle deployment can be further supported through public funding for research and Policy recommendations 23 HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT development, addressing a weak electricity grid, strengthening charging infrastructure, improving coordination between electric vehicle policy and customs duty policies, and increasing low-emission standards to incentivize switching to electric vehicles. Electric vehicles could strengthen Pakistan’s grid with appropriate upgrades: smart charging, vehicle to grid integration, and distributed storage. Awareness of the electricity generation mix is important, to avoid generating additional outdoor air pollution from fossil-fuel-based electricity. 5. Fully phase out fossil fuel subsidies by 2025. Fossil fuel subsidy reform efforts need to continue, as they can generate significant savings to support higher renewable energy ambitions. A priority when phasing out subsidies to fossil fuels or increasing prices through a carbon tax is to consider the impacts on people living in poverty and vulnerable people and address these inequities using targeted measures. These measures can take several forms, depending on the main needs of these population groups. Some examples are: targeted cash transfers, subsidized public transport, targeted subsidies to clean energy (for example, LPG or solar lamps), and subsidies to implement energy efficiency measures. Lastly, it is important to avoid setbacks in fossil fuel subsidy reform to give consistent price and investment signals, in addition to maintaining the health and climate benefits of the reforms. 6. Introduce a 10% fossil fuel tax by 2025. Key benefits of a carbon tax are that it helps reduce air pollution from fossil fuel use with associated health gains; creates incentives for redirecting investments towards cleaner power; helps meet climate change mitigation commitments; brings significant public revenue; and is straightforward to implement, for example, by integrating it into other existing road fuel taxes or extraction charges, or by introducing emission-trading systems. These policies have a major role to play in creating a levelled-playing field for clean energy alternatives and they raise significant funds that can be used to support other health and development priorities, so their role should not be disregarded. 7. Reinvest revenues in health. Revenues from carbon tax or fossil fuel subsidy reform can be earmarked for specific health-related purposes, similar to taxes to tobacco in some countries. Taxes on fuels can also be implemented to generate funds for specific purposes, such as to support COVID-19 recovery. It should be noted that this revenue would be expected to decrease over time and therefore considered a short-term revenue stream. By implementing the following four health recommendations, the Government of Pakistan could ensure health considerations remain a focus; contribute positively to climate ambition; maximize synergies and optimize trade- offs between climate, economic and health objectives; and thereby support a more coherent overall approach to sustainable development. 1. Put health at the centre of Pakistan’s NDC, in line with WHO guidance and recent research. Focusing NDC on health could help achieve health co-benefits and establish connections between health and other sectors crucial to climate change mitigation (Hamilton et al., 2021). Putting health in NDC ambition would not only estimate the accrued health benefits but also serve as a vehicle for climate change mitigation and adaptation. 2. Adopt a Health in All Policies (HiAP) approach to energy policy. HiAP “systematically takes into account the health and health systems implications of decisions, seeks synergies and avoids harmful health impacts, in order to improve population health and health equity” (Cook et al., 2013). In addition, diverting the saved resources towards the health of the population has benefits including, but not limited to, raising awareness about the linkages of climate change and health, adding weight to the cause. 3. Establish mechanisms to facilitate collaboration between health and energy professionals. Ambitious GHG emission-reduction policy needs to be combined with policies to realize health co-benefits in key sectors (Frost, 2021). Dialogue and collaboration between the Ministries of Health, Climate and Energy are key to ensuring the interlinkages among these three areas are properly included in political decisions. Working together HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT24 to inform the population about the benefits of specific policy options (for example, fossil fuel subsidy reform or fuel taxation) is important, as these options require an explanation of the rationale and co-benefits (Beaton et al., 2013). 4. Continue to obtain reliable data on health co-benefits of climate ambition in Pakistan to inform policies in various sectors. There is a key role for the health sector in providing and communicating to different stakeholders “authoritative and evidence-based advice about health risks and benefits associated with different climate mitigation policies” (World Health Organization, 2014a). This entails building the capacity of health professionals to document linkages between energy uses, climate change and health to identify climate-related causes of specific diseases, raise awareness among the population, and collect empirical data for relevant national and sectoral documentation. 25 HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT As the climate crisis and its threat to health become more apparent and evidence grows on the potential health benefits of climate mitigation action, further research in line with this study is encouraged. Lessons learned from this study are summarized here and can be extrapolated for further research activities in Pakistan or similar studies in other countries. 1. Creating multidisciplinary teams can help tackle the complex interrelations between different aspects of climate and health. The study modelled the connections between energy production, GHG emissions, and health outcomes, which required expertise in relevant sectors. That expertise came from national experts (Pakistan’s Ministry of National Health Services, Regulations and Coordination, Ministry of Climate Change, and Aga Khan University), international organizations and experts (the World Health Organization, International Institute of Sustainable Development, KnowlEdge Srl, and Spadaro Environmental Research Consultants). 2. Focusing on multiple co-benefits of ambitious climate policies, including environmental, health and economic benefits, can highlight the advantages of such policies to policymakers and the public. This study found that the high ambition scenario would result in GHG reductions of approximately 27.5% in 2030 compared to the NDC reference case scenario. GHG emissions reductions could prevent more than 65,000 deaths annually in 2030 and avoid 9,100 pregnancy losses attributed to air pollution. In economic terms, health gains from the high ambition pathway would be equivalent to 1.5% of Pakistan’s projected GDP in 2030. At the same time, governments should consider the high cost of inaction. Even the most conservative scenario considered in this study, the BAU scenario (which includes no additional policies, and investment as per historical trends) implies significant achievements in terms of postponed deaths and economic benefits versus the no action scenario. 3. Using international examples to inform the analysis can be useful in helping audiences understand the successes of climate and energy policies, gaps in those policies, and related opportunities for their own country. For this study, multiple examples were analysed, including China’s NDC and five-year plans, Mexico’s NDC and pollutant reduction strategy, Indonesia’s fossil fuel subsidy reforms, and examples from other countries. However, more studies are needed, and countries should be encouraged to produce similar simulations. In many cases, the availability of national and detailed data is presented as a hurdle. But, while national and detailed data allow for a better estimation of the modelling outcomes, this should not be a limiting factor to conduct these types of studies. There are international data available and other approximations that can provide some notion of the health impacts of climate policies, and which provide sufficient inputs in climate negotiations and policy- making processes. Lessons learned HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT26 Ali N & Islam F (2020). The effects of air pollution on COVID-19 infection and mortality – a review on recent evidence. Frontiers in Public Health. 8. (https://doi.org/10.3389/fpubh.2020.580057). Bassi A (2015). Moving towards integrated policy formulation and evaluation: The green economy model. Environmental and Climate Technologies, 16(1), 5–19. (https://doi.org/10.1515/rtuect-2015-0009). Beaton C, Gerasimchuk I, Laan T, Vis-Dunbar D, Wooders P & Lang K (2013). A guidebook to fossil-fuel subsidy reform for policy-makers in Southeast Asia. International Institute for Sustainable Development. (https://www.iisd.org/ publications/guidebook-fossil-fuel-subsidy-reform-policy-makers-southeast-asia, accessed 1 September 2021). Beaton C, Pradiptyo R, Susamto A, Wirotomo A & Adisasmita A (2016). Financing development with fossil fuel subsidies: the reallocation of Indonesia’s gasoline and diesel subsidies in 2015. International Institute for Sustainable Development. (https://www.iisd.org/system/files/2020-08/financing-development-with-fossil-fuel-subsidies- indonesia.pdf, accessed 1 September 2021). Burke J (2016). India introduces car sales tax to combat pollution. The Guardian. 29 February 2016. (http://www. theguardian.com/world/2016/feb/29/india-introduces-car-sales-tax-to-combat-pollution, accessed 1 September 2021). Burniaux J-M & Chateau J (2014). Greenhouse gases mitigation potential and economic efficiency of phasing-out fossil fuel subsidies. International Economics. 140:71–88. Cabinet of the President of the Republic of Suriname (2019). The Republic of Suriname: Nationally Determined Contribution 2020. Cabinet of the President of the Republic of Suriname. (https://www4.unfccc.int/sites/ ndcstaging/PublishedDocuments/Suriname%20Second/Suriname%20Second%20NDC.pdf, accessed 1 September 2021). Cai W, Hui J, Wang C, Zheng Y, Zhang X, Zhang Q et al. (2018). The Lancet Countdown on PM 2·5 pollution-related health impacts of China’s projected carbon dioxide mitigation in the electric power generation sector under the Paris Agreement: a modelling study. Lancet Planet Health. 2(4):e151–e161. (https://doi.org/10.1016/S2542- 5196(18)30050-0). Campbell-Lendrum D & Prüss-Ustün A (2018). Climate change, air pollution and noncommunicable diseases. Bulletin of the World Health Organization. 97:160–161. Climate & Clean Air Coalition (2020) Mexico launches National Strategy to Reduce Short-Lived Climate Pollutants for climate change, air quality and human health. CCAC. 24 February 2020. (https://www.ccacoalition.org/en/news/mexico-launches- national-strategy-reduce-short-lived-climate-pollutants-climate-change-air, accessed 1 September 2021). Cook S, Leppo K, Ollila E, Peña S & Wismar M (Leppo, K, Ed.) (2013). Health in all policies: seizing opportunities, implementing policies. Helsinki: Ministry of Social Affairs and Health of Finland. (https://www.unrisd. org/80256B3C005BCCF9/search/5416E4680AD46606C1257B730038FAC1, accessed 1 September 2021). Council of the European Union (2021). Council conclusions on Climate and Energy Diplomacy –delivering on the external dimension of the European Green Deal. Brussels, 25 January 2021. (https://www.consilium.europa.eu/ media/48057/st05263-en21.pdf, accessed 1 September 2021). Deccan Herald. (2020). Pakistan eyes 60% clean energy by 2030: PM Imran Khan. Deccan Herald. 13 December 2020. (https://www.deccanherald.com/international/world-news-politics/pakistan-eyes-60-clean-energy-by-2030-pm- imran-khan-926769.html, accessed 1 September 2021). References 27 HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT Frost E (2021). Realising the health opportunities of COVID-19 recovery plans through Nationally Determined Contributions. The Lancet countdown on health and climate change. (https://www.lancetcountdown.org/ resources/, accessed 1 September 2021). G20 Research Group (2009). G20 Leaders’ Statement: The Pittsburgh Summit. September 24-25, 2009, Pittsburgh. (http://www.g20.utoronto.ca/2009/2009communique0925.html, accessed 1 September 2021). Gass P, Gerasimchuk I, Wooders P, Garg V, Bassi A, Viswanathan B et al. (2019). Raising ambition through fossil fuel subsidy reform: greenhouse gas emissions results modelling from 26 countries. International Institute for Sustainable Development. (https://www.iisd.org/system/files/publications/raising-ambition-through-fossil-fuel-subsidy-reform. pdf, accessed 1 September 2021). Gould T & Adam Z (2020). Low fuel prices provide a historic opportunity to phase out fossil fuel consumption subsidies – analysis. International Energy Agency. 2 June. (https://www.iea.org/articles/low-fuel-prices-provide-a- historic-opportunity-to-phase-out-fossil-fuel-consumption-subsidies, accessed 1 September 2021). Hamilton I, Kennard H, McGushin A, Höglund-Isaksson L, Kiesewetter G, Lott M et al. (2021). The public health implications of the Paris Agreement: a modelling study. Lancet Planet Health. 5(2):e74–e83. (https://doi.org/10.1016/ S2542-5196(20)30249-7). Health Effects Institute (2020). State of Global Air 2020. Special Report. Boston, MA: Health Effects Institute. (https:// www.stateofglobalair.org/sites/default/files/documents/2020-10/soga-2020-report-10-26_0.pdf, accessed 1 September 2021). Institute for Health Metrics and Evaluation (2021). Global Health Data Exchange. (http://ghdx.healthdata.org/, accessed 1 September 2021). Intergovernmental Panel on Climate Change. (2007). Fourth Assessment Report (AR4). (https://www.ipcc.ch/ assessment-report/ar4/, accessed 1 September 2021). International Energy Agency (2020). World Energy Outlook 2020. (https://www.iea.org/reports/world-energy- outlook-2020, accessed 1 September 2021). International Monetary Fund (2018). Country-level energy subsidy estimates. (https://www.imf.org/en/Topics/climate- change/energy-subsidies, accessed 1 September 2021). International Renewable Energy Agency (2020). Renewable power generation costs in 2019. International Renewable Energy Agency, Abu Dhabi. (https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2020/Jun/IRENA_ Power_Generation_Costs_2019.pdf, accessed 1 September 2021). Iqbal A (2020). Pakistan will have 30pc electric vehicles by 2030. DAWN. 8 December. (https://www.dawn.com/ news/1594548, accessed 1 September 2021). Merrill L, Harris M, Casier L & Bassi A (2015). Fossil-fuel subsidies and climate change. Nordic Council of Ministers. (https://www.iisd.org/publications/tackling-fossil-fuel-subsidies-and-climate-change-levelling-energy-playing-field, accessed 1 September 2021). Ministry of Environment and Natural Resource (2020). Nationally Determined Contributions: 2020 update. Government of Mexico. (https://www4.unfccc.int/sites/ndcstaging/PublishedDocuments/Mexico%20First/NDC- Eng-Dec30.pdf, accessed 1 September 2021). Ministry of Mahaweli Development and Environment (2016). Nationally Determined Contributions. Ministry of Mahaweli Development and Environment, Sri Lanka. (https://www4.unfccc.int/sites/ndcstaging/ PublishedDocuments/Sri%20Lanka%20First/NDCs%20of%20Sri%20Lanka.pdf, accessed 1 September 2021). National Energy Efficiency and Conservation Authority, Ministry of Energy of Pakistan (2020). Strategic Plan (2020– 2023). (https://www.neeca.gov.pk/SiteImage/Misc/files/NEECA%20Strategic%20Plan%202020-23%20Final%20 28%20October%202020(1).pdf, accessed 1 September 2021). HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT28 Organisation for Economic Co-operation and Development (2012). Mortality risk valuation in environment, health and transport policies. Paris, OECD Publishing. (https://www.oecd.org/env/tools-evaluation/ mortalityriskvaluationinenvironmenthealthandtransportpolicies.htm, accessed 1 September 2021). Sanchez L & Wang H (2020). Health co-benefits from NDC implementation in China. International Institute for Sustainable Development. (https://www.iisd.org/publications/health-co-benefits-ndc-implementation-china, accessed 1 September 2021). Schwanitz VJ, Piontek F, Bertram C & Luderer G (2014). Long-term climate policy implications of phasing out fossil fuel subsidies. Energy Policy. 67:882–894. (https://doi.org/10.1016/j.enpol.2013.12.015). Stockholm Environment Institute, International Institute for Sustainable Development, Overseas Development Institute, Third Generation Environmentalism & United Nations Environment Programme (2020). The production gap report: 2020 special report. (https://productiongap.org/2020report/, accessed 1 September 2021). Silva RA, West JJ, Lamarque J-F, Shindell DT, Collins WJ, Faluvegi G et al. (2017). Future global mortality from changes in air pollution attributable to climate change. Nature Climate Change. 7(9):647–651. (https://doi.org/10.1038/ nclimate3354). Smith K, Woodward A, Campbell-Lendrum D, Chadee D, Honda Y, Liu Q et al. (2014). Human health: impacts, adaptation, and co-benefits. In Climate change 2014: impacts, adaptation, and vulnerability. Part A: Global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. pp.709–745. (https://www.ipcc.ch/site/assets/uploads/2018/02/WGIIAR5-Chap11_FINAL.pdf, accessed 1 September 2021). Spadaro JV, Sanchez Martinez G & Kendrovski V (2018). Achieving health benefits from carbon reductions: Manual for CaRBonH calculation tool. World Health Organization. (https://www.euro.who.int/__data/assets/pdf_ file/0004/386923/health-carbon-reductions-eng.pdf, accessed 1 September 2021). Terton A, Gass P, Merrill L, Wagner A & Meyer E (2015). Fiscal instruments in INDCs: how countries are looking to fiscal policies to support INDC implementation. International Institute for Sustainable Development. (https://www.iisd. org/system/files/publications/fiscal-instruments-indcs.pdf, accessed 1 September 2021 Uddin M (2020). Pakistan’s national electric vehicle policy: charging towards the future. The International Council on Clean Transportation. 10 January 2020. (https://theicct.org/blog/staff/pakistan%E2%80%99s-national-electric- vehicle-policy-charging-towards-future, accessed 1 September 2021). United Nations (2021). End fossil fuel subsidies, bolster funding for renewable energy particularly in Africa, Secretary- General tells round table on clean power transition. UN. 11 January 2021. (https://www.un.org/press/en/2021/ sgsm20530.doc.htm, accessed 1 September 2021). United Nations – World Population Prospects (2019). World Population Prospects 2019. (https://population.un.org/ wpp/, accessed 1 September 2021). United Nations Framework Convention on Climate Change (2021). NDC Registry. (https://www4.unfccc.int/sites/ ndcstaging/Pages/Home.aspx, accessed 1 September 2021). Varadhan S & Ahmed A (2020). India increases fuel taxes in a bid to shore up revenue. Reuters. 14 March 2020. (https://www.reuters.com/article/us-india-economy-fuel-idUSKBN21108I, accessed 1 September 2021). Vohra K, Vodonos A, Schwartz J, Marais EA, Sulprizio MP & Mickley LJ (2021). Global mortality from outdoor fine particle pollution generated by fossil fuel combustion: results from GEOS-Chem. Environmental Research. 195 (110754). (https://doi.org/10.1016/j.envres.2021.110754). Watts N, Amann M, Arnell N, Ayeb-Karlsson S, Beagley J, Belesova K et al. (2021). The 2020 report of The Lancet Countdown on health and climate change: responding to converging crises. The Lancet. 397(10269):129–170. (https://doi.org/10.1016/S0140-6736(20)32290-X). 29 HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT World Bank (2020). State and Trends of Carbon Pricing 2020 (May), World Bank, Washington, DC. (https://doi. org/10.1596/978-1-4648-1586-7). World Bank Group (2021a) GDP per capita, PPP (current international $). (https:// data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD, accessed 1 September 2021). World Bank Group. (2021b). World Bank Open Data. (https://data.worldbank.org/indicator/EN.ATM.CO2E. KT?locations=PK-LK&most_recent_value_desc=true, accessed 1 September 2021). World Health Organization (2014a). Promoting health while mitigating climate change: Technical Briefing for the World Health Organization. Conference on Health and Climate, Geneva. (https://cdn.who.int/media/docs/default- source/climate-change/discussion-paper---promoting-health-while-mitigating-climate-change.pdf, accessed 1 September 2021) World Health Organization (2014b). 7 million premature deaths annually linked to air pollution. WHO. 25 March 2014. (https://www.who.int/mediacentre/news/releases/2014/air-pollution/en/, accessed 1 September 2021). World Health Organization (2020a). Health in National Determined Contributions (NDCs): a WHO review. Geneva: World Health Organization; 2020. (https://www.who.int/publications-detail-redirect/who-review-health-in-the- ndcs, accessed 1 September 2021). World Health Organization (2020b). WHO manifesto for a healthy recovery from COVID-19. Geneva: World Health Organization; 2020. L (https://www.who.int/news-room/feature-stories/detail/who-manifesto-for-a-healthy- recovery-from-covid-19, accessed 1 September 2021). Xue T, Guan T, Geng G, Zhang Q, Zhao Y & Zhu T (2021). Estimation of pregnancy losses attributable to exposure to ambient fine particles in south Asia: an epidemiological case-control study. Lancet Planet Health. 5(1):e15–e24. (https://doi.org/10.1016/S2542-5196(20)30268-0). Wind Measurement mast at a FFCEL wind farm, Pakistan ©ESMAP World Bank Annexes 31 HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT A nn ex 1 Em iss io n fa ct or s u se d fo r t he e st im at io n of a ir po llu ta nt s f ro m e ne rg y co ns um pt io n an d po w er g en er at io n Fu el ty pe / se ct or CO 2 CO N O x N M V O C CH 4 PM 10 PM 2. 5 Bl ac k ca rb on O rg an ic ca rb on N 2O N H 3 To n/ TJ kg /T J Co al Re sid en tia l 94 .6 2, 61 0 34 48 4 30 0 49 0. 1 44 0. 4 72 .8 5 19 6. 4 0 38 .7 4 Co m m er ci al 94 .6 93 1 17 3 88 .8 10 11 7 10 8 6. 9 5. 2 1. 5 0. 00 93 In du st ry 94 .6 93 1 17 3 88 .8 10 11 7 10 8 6. 9 5. 2 1. 5 0. 00 93 Tr an sp or t N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A N /A Pe tr ol eu m Re sid en tia l 67 .4 6 93 30 6 20 10 21 18 10 .1 2. 89 1. 51 0. 10 8 Co m m er ci al 74 .1 13 0 94 2 50 10 96 96 40 28 0. 6 0. 16 8 In du st ry 74 .1 66 51 3 25 3 20 20 11 .2 3. 6 0. 6 0. 15 4 Tr an sp or t 69 .3 5, 80 8 64 4. 2 74 1. 81 33 16 1. 64 16 1. 64 9. 27 11 6. 38 3. 2 31 .0 3 N at ur al g as Re sid en tia l 56 .1 26 51 1. 9 5 1. 2 1. 2 0. 06 5 0. 54 0. 1 0. 18 1 Co m m er ci al 56 .1 29 74 23 5 0. 78 0. 78 0. 03 0. 26 0. 94 2 1. 21 4 In du st ry 56 .1 29 74 23 1 0. 78 0. 78 0. 03 0. 26 0. 1 1. 21 4 Tr an sp or t 56 .1 27 1. 7 54 3. 48 12 .1 4 92 0. 72 5 0. 72 5 0. 03 6 0. 32 6 2. 72 N /A Bi of ue ls a nd w as te Re sid en tia l 95 .6 4, 75 3 13 4. 6 1, 65 4 30 0 51 2. 35 40 9. 88 51 .2 4 17 8. 4 13 4. 57 53 .7 Co m m er ci al 11 2 57 0 91 30 0 30 0 14 3 14 0 39 .2 72 4 37 In du st ry 11 2 57 0 91 30 0 30 14 3 14 0 39 .2 72 4 37 Tr an sp or t 79 .6 5, 80 8 51 0. 69 18 1. 9 1. 9 0. 16 0 0 0 Po w er g en er at io n (b y fu el ty pe ) Fu el a nd d ie se l o il 77 .4 15 .1 14 2 2. 3 3 25 .2 19 .3 0. 95 7 0. 35 9 0. 6 0. 10 1 Co ge ne ra tio n 77 .4 15 .1 14 2 2. 3 3 25 .2 19 .3 0. 95 7 0. 35 9 0. 6 0. 10 1 N at ur al g as 56 5. 1 39 89 2. 6 1 0. 89 0. 89 0. 02 2 0. 02 2 0. 1 1. 82 9 Co al 96 .1 8. 7 24 7 1. 4 1 7. 9 3. 2 0. 08 3 0. 16 7 1. 5 0. 01 2 W as te (M SW ) 91 .7 90 81 7. 31 30 15 5 13 3 1. 44 4 0. 22 2 4 0. 22 2 HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT32 Annex 2 Overview of air pollutants from energy consumption and power generation Scenario / pollutant Unit 2020 2030 Difference vs NDC Reference (%) 2050 Difference vs NDC Reference (%) Total black carbon emissions from energy consumption and power generation NDC Reference t/year 79 249 93 802 106 762 BAU t/year 79 249 89 052 -5.1% 91 204 -14.6% Current ambition t/year 79 249 82 957 -11.6% 68 313 -36.0% High ambition t/year 79 249 72 938 -22.2% 42 869 -59.8% Total CH4 emissions from energy consumption and power generation NDC Reference t/year 402 229 462 573 497 518 BAU t/year 402 229 437 872 -5.3% 421 078 -15.4% Current ambition t/year 402 229 405 316 -12.4% 311 096 -37.5% High ambition t/year 402 229 354 238 -23.4% 182 263 -63.4% Total CO emissions from energy consumption and power generation NDC Reference t/year 9 681 603 12 240 914 15 242 258 BAU t/year 9 681 603 9 023 429 -26.3% 9 316 885 -38.9% Current ambition t/year 9 681 603 8 164 594 -33.3% 6 127 120 -59.8% High ambition t/year 9 681 603 7 137 963 -41.7% 3 493 713 -77.1% Total CO2 emissions from energy consumption and power generation NDC Reference t/year 300 761 756 395 093 832 526 139 280 BAU t/year 300 761 756 365 443 132 -7.5% 451 363 576 -14.2% Current ambition t/year 300 761 756 326 953 132 -17.2% 333 205 148 -36.7% High ambition t/year 300 761 756 286 736 636 -27.4% 236 042 696 -55.1% Total N2O emissions from energy consumption and power generation NDC Reference t/year 8666 10 894 13 485 BAU t/year 8666 10 260 -5.8% 11 253 -16.5% Current ambition t/year 8666 9433 -13.4% 7636 -43.4% High ambition t/year 8666 8273 -24.1% 4723 -65.0% Total NH3 emissions from energy consumption and power generation NDC Reference t/year 90 298 108 089 123 780 BAU t/year 90 298 102 217 -5.4% 101 718 -17.8% Current ambition t/year 90 298 94 788 -12.3% 66 917 -45.9% High ambition t/year 90 298 82 841 -23.4% 36 809 -70.3% Total NMVOC emissions from energy consumption NDC Reference t/year 2 535 938 3 018 402 3 434 790 BAU t/year 2 535 938 2 857 407 -5.3% 2 836 322 -17.4% Current ambition t/year 2 535 938 2 650 233 -12.2% 1 904 379 -44.6% High ambition t/year 2 535 938 2 313 260 -23.4% 1 046 774 -69.5% 33 HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT Scenario / pollutant Unit 2020 2030 Difference vs NDC Reference (%) 2050 Difference vs NDC Reference (%) Total NOx emissions from energy consumption and power generation NDC Reference t/year 863 060 1 204 681 1 716 195 BAU t/year 863 060 1 009 941 -16.2% 1 276 383 -25.6% Current ambition t/year 863 060 788 191 -34.6% 770 441 -55.1% High ambition t/year 863 060 689 825 -42.7% 527 082 -69.3% Total organic carbon emissions from energy consumption and power generation NDC Reference t/year 299 760 362 281 421 881 BAU t/year 299 760 343 045 -5.3% 345 269 -18.2% Current ambition t/year 299 760 318 447 -12.1% 222 705 -47.2% High ambition t/year 299 760 278 035 -23.3% 119 773 -71.6% Total PM10 emissions from energy consumption and power generation NDC Reference t/year 794 267 952 557 1 105 391 BAU t/year 794 267 902 591 -5.2% 931 402 -15.7% Current ambition t/year 794 267 837 340 -12.1% 665 036 -39.8% High ambition t/year 794 267 733 695 -23.0% 399 726 -63.8% Total PM2.5 emissions from energy consumption and power generation NDC Reference t/year 664 847 805 248 949 134 BAU t/year 664 847 763 467 -5.2% 797 905 -15.9% Current ambition t/year 664 847 708 416 -12.0% 562 111 -40.8% High ambition t/year 664 847 621 095 -22.9% 337 961 -64.4% Total SO2 emissions from energy consumption and power generation NDC Reference t/year 1 380 136 2 191 543 3 656 465 BAU t/year 1 380 136 2 073 836 -5.4% 3 319 176 -9.2% Current ambition t/year 1 380 136 1 899 161 -13.3% 2 756 785 -24.6% High ambition t/year 1 380 136 1 722 401 -21.4% 2 239 203 -38.8% Annex 2 contd. Overview of air pollutants from energy consumption and power generation HEALTH BENEFITS OF RAISING AMBITION IN PAKISTAN’S NATIONALLY DETERMINED CONTRIBUTION: WHO TECHNICAL REPORT34 Power generation technologies Diesel and fuel oil Hydropower (large scale) Cogeneration Hydropower (small scale) Gas turbine Solar power (utility scale) Steam coal Solar PV (rooftop) Nuclear Wind (onshore) Biomass Wind (offshore) Waste generation Annex 3 Power generation technologies considered in GEM
For further information please contact: Climate Change and Health Unit Department of Environment, Climate Change and Health World Health Organization Email: climatehealth@who.int Webpage: https://www.who.int/health-topics/climate-change