Editing IPCC:AR6/WGIII/Chapter-17 (section)

==== 17.3.2.1 Model Assessments on the Sustainable Development Pathways for Decarbonisation ====

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This section assesses the model evaluations of the sustainable development pathways for decarbonisation, including the co-benefits and trade-offs involving explorations of alternative future development pathways as a basis for clarifying societal objectives and understanding the restrictions. Shifting development pathways to increased sustainability involves a number of complex issues, which are difficult to integrate into models. For a more detailed discussion about this, see [[IPCC:Wg3:Chapter:Chapter-4#4.4.1|Section 4.4.1]] and Cross-Chapter Box 5 in Chapter 4.

Development pathways that focus narrowly on climate mitigation or economic growth will not lead to the SDGs and long-term climate-stabilisation objectives being achieved. The best chances of doing this lie in development pathways that can maximise the synergies between climate mitigation and sustainable development more broadly ( [[IPCC:Wg3:Chapter:Chapter-1#1.3.2|Section 1.3.2]] ). Areas of focal modelling include green investments, technological change, employment generation and the performance of policy instruments, such as green taxes, subsidies, emission permits, investments and finance. Short- and long-term macroeconomic models have been used to assess the impacts of such policy instruments. [[#Jaumotte--2021|Jaumotte et al. (2021)]] analyse the economic impacts on net zero emissions by 2050 with a focus on short-term economic policies and the integration of climate policies such as CO 2 taxes with green reform policies. This may imply the co-creation of benefits between climate policy objectives, and macroeconomic policy goals such as employment creation.

There is an emerging modelling literature focusing on the synergies and trade-offs between low-carbon development pathways and various aspects of sustainable development. The early literature, including that on IAMs, and macroeconomic and sectoral models, mainly focused on the co-benefits of mitigation policies in terms of reduced air pollution, energy security and to some extent employment generation security ( [[#IPCC--2014|IPCC 2014]] , 2018c) (Chapter 6). Some models have been developed further with assessments of a broader range of the joint benefits of mitigation, health, water, land use and food security ( [[#Clarke--2014|Clarke et al. 2014]] ; [[#IPCC--2014|IPCC 2014]] , 2018; [[#Kolstad--2014|Kolstad et al. 2014]] ). According to Chapter 1, there is a need to incorporate issues and enablers further, including a wide range of non-climate risks, varying forms of innovation, possibilities for behavioural and social change, feasible policies and equity issues (Executive Summary in Chapter 1).

IAMs and macroeconomic models typically calculate mitigation costs based on the assumption that markets internalise externalities like GHG emissions through carbon prices ( [[#Barker--2016|Barker et al. 2016]] ; [[#IEA--2017|IEA 2017]] , 2019). Yet, there are legitimate questions to be asked about whether carbon pricing will be efficient if markets are inefficient ( [[#World%20Bank--2019|World Bank 2019]] ). However, market inefficiencies are difficult to integrate into the models. How GHG emissions taxes would actually work is thus quite uncertain based on the modelling studies ( [[#Barker--2016|Barker et al. 2016]] ; [[#Fontana--2016|Fontana and Sawyer 2016]] ; [[#Meyer--2018|Meyer et al. 2018]] ). Despite these limitations, the use of GHG emission taxes as an effective instrument based on modelling results in practice has implications for public policies and private-sector investments.

Despite the shortcomings of conventional economic thought and models already pointed out, improved models have demonstrated new perspectives on how mitigation costs can be assessed in macroeconomic models. For instance, while a conventional perspective might suggest that climate change mitigation costs can limit investments in sustainability because they reduce the productivity of capital by increasing energy prices and the products in which energies are embodied, another perspective is that innovation can imply increases in efficiency and that the substitution of energy, material and labour can lead to the accumulation of capital and productivity gains. This appears to occur with innovations in end-use energy applications generating emissions reductions and delivering on other sustainable development benefits ( [[#Wilson--2019|Wilson et al. 2019]] ). Similarly, IAM models have been applied to model the potential for Low Energy Demand (LED) scenarios associated with demand-side innovations in the service sector. ( [[#Grubler--2018|Grubler et al. 2018]] ) have developed a climate-friendly LED scenario which assumes information technology innovations such as the internet of things (IoT) and induced social changes such as the sharing economy. Nonetheless there are still very important limits on the degree to which highly aggregated IAM models and macroeconomic models can integrate ethics, equity and several other key policy-relevant aspects of sustainable development ( [[#Easterlin--2010|Easterlin et al. 2010]] ; [[#Koch--2020|Koch 2020]] ). A key limitation in this context is that, while all countries share the totality of the SDGs, development priorities differ across countries and over time. Moreover, these priorities are strongly linked to local contexts, and this can only be reflected directly in national models ( [[IPCC:Wg3:Chapter:Chapter-4#4.3.2|Section 4.3.2]] ).

An example of a project that assesses the economy-wide impacts of linking sustainable development with deep decarbonisation is the Deep Decarbonisation Pathways Project (DDPP) ( [[#Bataille--2016|Bataille et al. 2016]] ), which is undertaking a comparative assessment of studies of 16 countries representing more than 74% of global energy-related emissions for the pathway to 2ºC stabilisation scenarios. The DDPP’s methodology is to combine scenario analysis in different national contexts using macroeconomic models and sectoral models and to facilitate a consistent cross-country analysis using a set of common assumptions.

The key conclusions of the DDPP team on the economy-wide impacts are that country-based studies such as South Africa’s demonstrate that it is possible to improve income distribution, alleviate poverty and reduce unemployment while simultaneously transitioning to a low-carbon economy ( [[#Altieri--2016|Altieri et al. 2016]] ). The DDPP in Japan explores whether energy security can be enhanced through increases in renewable energy ( [[#Oshiro--2016|Oshiro et al. 2016]] ). The reduction of uncontrolled fossil fuel emissions has significant public-health benefits according to the Chinese and Indian DDPPs, as fossil fuel combustion is the major source of air pollution.

For example, in the Chinese DDPP, deep decarboniation scenarios have resulted in reductions of 42–79% in primary air pollutants (e.g., SO 2 , NO x , particulate matter (PM2.5), volatile organic compounds (VOCs), and NH 3 ), thus meeting air-quality standards in major cities. The deep decarbonisation scenarios include the large and fast energy-efficient improvements required to improve energy access and affordability. The DDPP studies are thus an example of an approach in which national deep-carbonisation scenarios are linked to the development goals of income generation, energy access and affordability, employment, health and environmental policy.

Sustainable development scenarios have also been developed by the Low-Carbon Society’s (LCS) assessments ( [[#Kainuma--2012|Kainuma et al. 2012]] ), in which multiple sustainable development and climate change mitigation goals were assessed jointly. The scenario analysis was conducted for Asian countries such as South Korea, Japan, India, China and Nepal with a soft linked IAM using economy-wide and sectoral models and linked to very active stakeholder engagement in order to reflect national policy perspectives and priorities. Some of the models are economy-wide global IAMs, while others are national partial equilibrium models.

The LCS scenarios also include a specific attempt to include ongoing dialogues with policymakers and stakeholders in order to reflect governance and enabling factors, and to enable the modelling processes to reflect political realism as far as possible. Diverse stakeholders who acted as validators of the scientific process were included, stakeholder preferences were revealed, and recipients and users of the LCS outputs were included in ongoing dialogues on outputs and in interpreting the results. The aim of the stakeholder interactions was thus to fill the gap between typical laboratory-style IAMs and down-scaled but unaligned practical assessments performed at disaggregated geographical and sector-specific scales.

Energy scenarios for sustainable development were included in The World Energy Outlook of the IEA ( [[#IEA--2019|IEA 2019]] , 2020) in terms of a Sustainable Development Scenario (SDS), which assessed not only SDG 13 (climate action) but also SDG 7 (affordable and clean energy) and SDG 3.9 (air pollution). This scenario takes as its starting point the policy goal of meeting these SDGs and then assesses the costs of meeting an emissions reduction target of 70% of CO 2 from the energy system by 2030. The scenario concludes that retrofitting coal-fired power plants with pollution controls is the cheapest option for dealing with local pollution in the short term, but that this is not consistent with meeting the long-term emissions goals of the Paris Agreement. The SDS scenario combines the goal of reducing the amount of CO 2 in the energy system by 70%, with large decreases in energy-related emissions of NO X , SO 2 and PM 2.5 , leading to a fall of 40–60% by 2030, and to 2.5 million fewer premature deaths from air pollution in 2030 than in the Stated Policies Scenario (STEPS), which represent a continuation of current trends in the energy system ( [[#IEA--2020|IEA 2020]] ).

The costs of energy-system transitions have been assessed by several energy-system studies. The economic costs of meeting the different goals depend on the stringency of the mitigation target, as well as economic (fuel prices, etc.) and technological developments (technology availability, capital costs, etc.). In addition, changes in infrastructure and behavioural patterns and lifestyles matter. Model-based assessments vary, depending on these assumptions and differences in modelling approaches ( [[#Krey--2019|Krey et al. 2019]] ) ( [[IPCC:Wg3:Chapter:Chapter-6#6.7.7|Section 6.7.7]] ). Country characteristics determine the social, economic and technical priorities for low-emission pathways. Domestic policy circumstances impact on pathways and costs, for example, when affordability and energy-security concerns are emphasised ( [[#Oshiro--2016|Oshiro et al. 2016]] ).

Mitigation policies can have important distributive effects between and within countries, and may affect impact on the poorest through their effects on energy and food prices ( [[#Hasegawa--2018|Hasegawa et al. 2018]] ; [[#Fujimori--2019|Fujimori et al. 2019]] ) ( [[IPCC:Wg3:Chapter:Chapter-3#3.6.4|Section 3.6.4]] ), while higher levels of warming are projected to generate higher inequality between countries as well as within them (Chapter 16). Mitigation thus can reduce economic inequalities and poverty by avoiding such impacts ( [[IPCC:Wg3:Chapter:Chapter-3#3.6.4|Section 3.6.4]] ).

Improved air quality and the associated health effects are the co-benefit category dominating model-based assessments of co-benefits, but a few studies have also covered other aspects, such as the health effects of dietary change and biodiversity impacts (Sections 3.6.3 and 17.3). Mitigation has implications for global economic inequalities through different channels and can compound or lessen inequalities, avoid impacts and create co-benefits that reduce inequalities ( [[IPCC:Wg3:Chapter:Chapter-3#3.6.4|Section 3.6.4]] ). There are, however, several challenges involved in balancing the dilemmas associated with meeting the SDGs, such as, for example, energy access, equity and sustainability. Fossil fuel-dependent developing countries cannot transition to low-carbon economics without considering the wider impacts on development by doing so ( [[IPCC:Wg3:Chapter:Chapter-3#3.7.3|Section 3.7.3]] ).

Climate change has negative impacts on agricultural productivity in general, including unequal geographical distribution (Chapter 3). On top of that, there is also a risk that climate change mitigation aimed at achieving stringent climate goals could negatively affect food access and food security ( [[#Akimoto--2012|Akimoto et al. 2012]] ; [[#Fujimori--2019|Fujimori et al. 2019]] ; [[#Hasegawa--2018|Hasegawa et al. 2018]] ). If not managed properly, the risk of hunger due to climate policies such as large-scale bioenergy production increases remarkably if the 2°C and 1.5°C targets are implemented ( [[IPCC:Wg3:Chapter:Chapter-3#3.7.1|Section 3.7.1]] ). Taking the highest median values from different IAMs for given classes of scenarios, up to 14.9 GtCO 2 yr –1 carbon dioxide removal (CDR) from BECCS is required in 2100, and 2.4 GtCO 2 yr –1 for afforestation. Across the different scenarios, median changes in global forest area throughout the 21st century reach the required 7.2 Mkm 2 increases between 2010 and 2100, and agricultural land used for second-generation bioenergy crop production may require up to 6.6 Mkm 2 in 2100, increasing the competition for land and potentially affecting sustainable development (AR6 scenarios database).

Reducing climate change can reduce the share of the global population exposed to increased stress from reductions in water resources ( [[#Arnell--2014|Arnell and Lloyd-Hughes 2014]] ) and therefore to water scarcity as defined by a cumulative abstraction-to-demand ratio ( [[#Hanasaki--2013|Hanasaki et al. 2013]] ). ( [[#Byers--2018|Byers et al. 2018]] ), show that 8–14% of the population will be exposed to severe reductions in water supply if average temperatures increase between 1.5°C and 2°C ( [[IPCC:Wg3:Chapter:Chapter-3#3.7.2|Section 3.7.2]] ). ( [[#Hayashi--2018|Hayashi et al. 2018]] ) assess the water availability for different emission pathways, including the 2°C and 1.5°C targets, in light of the various factors governing availability. There are very different impacts among nations. In Afghanistan, Pakistan and South Africa, water stress is estimated to increase by 2050 mainly due to increases in irrigation water associated with the rising demand for food, and climate change will already increase water stress within the next decades. Other factors, such as changes in the demand for municipal water, water for electricity generation, other industrial water, and water for livestock due to climate change mitigation, are of limited importance.

( [[#Vandyck--2018|Vandyck et al. 2018]] ) estimate that the 2°C pathway would reduce air pollution and avoid 0.7–1.5 million premature deaths in 2050 compared to current levels. It is generally agreed that in both developed and developing countries there are additional benefits of climate change mitigation in terms of improved air quality ( [[IPCC:Wg3:Chapter:Chapter-3#3.7.4|Section 3.7.4]] ). ( [[#Markandya--2018|Markandya et al. 2018]] ) assessed the health co-benefits of air pollution reductions and the mitigation costs of the Paris Agreement using global scenarios for up to 2050. They concluded that the health co-benefits substantially outweighed the policy costs of achieving the NDC targets and either 2°C or 1.5°C stabilisation. The ratio of health co-benefits to the mitigation costs ranged from 1.4 to 2.45, depending on the scenario. The extra effort of trying to pursue the 1.5°C target instead of the 2°C target would generate a substantial net benefit in some areas. In India, the co-health benefits were valued at USD3.28–8.4 trillion and those in China at USD0.27–2.31 trillion. ( [[#Gi--2019|Gi et al. 2019]] ) also show that developing countries such as India have a huge potential to produce co-benefits. In addition, this implies that while the cost advantages of simultaneously achieving reductions of CO 2 emissions and of PM 2.5 are clear, the advantages for integrated measures could be limited, as the costs greatly depend on the CO 2 emissions reduction target.

( [[#Grubler--2018|Grubler et al. 2018]] ) models a pathway leading to global temperature change of less than 1.5°C without carbon capture and storage (CCS), taking end-use changes into account, including innovations in information technologies and changes to consumer behaviour apart from passive consumption. The pathway estimates global final-energy demand of 245 EJ yr –1 in 2050, which is much lower than in existing studies ( [[IPCC:Wg3:Chapter:Chapter-5#5.3.3|Section 5.3.3]] ). It also shows the possibilities of creating synergies between multiple SDGs, including hunger, health, energy access and land use. Integrated technological and social innovations will increase the opportunity to achieve sustainable development. ( [[#Millward-Hopkins--2020|Millward-Hopkins et al. 2020]] ) estimate global final energy at 149 EJ yr –1 in 2050 as required to provide decent material living standards, which is much lower than the 1.5°C scenario ranges (330–480 EJ yr –1 in 2050) of IAMs ( [[#IPCC--2018|IPCC 2018]] ) and the 390 EJ yr –1 in the IEA SDS ( [[#IEA--2019|IEA 2019]] ), and also lower than ( [[#Grubler--2018|Grubler et al. 2018]] ). The conclusion is that, although providing material living standards does not guarantee that every person will live a good life, there are large potentials in achieving low energy demand with sustainable development.

An overview of the co-benefits and trade-offs of several SDGs based on modelling results is provided in Figure 3.39 ( [[IPCC:Wg3:Chapter:Chapter-3#3.7|Section 3.7]] ). Selected mitigation co-benefits and trade-offs are provided in relation to meeting the 1.5°C temperature goal based on a subset of models and scenarios, despite many IAMs so far not having comprehensive coverage of the Sustainable Development Goals ( [[#Rao--2017|Rao et al. 2017]] ; [[#van%20Soest--2019|van Soest et al. 2019]] ). There are several co-benefits of mitigation policies, including increased forest cover (SDG 15) and reduced mortality from ambient PM 2.5 pollution (SDG 3) compared to reference scenarios. However, mitigation policies can also cause higher food prices and thus increase the share of the global population at risk from hunger (SDG 2), while also relying on solid fuels (SDGs 7 and 3) as side effects. It is then concluded in [[IPCC:Wg3:Chapter:Chapter-3#3.7|Section 3.7]] that these trade-offs can be balanced through targeted support measures and/or additional SD policies ( [[#Bertram--2018|Bertram et al. 2018]] ; [[#Cameron--2016|Cameron et al. 2016]] ; [[#Fujimori--2019|Fujimori et al. 2019]] ).

The World in 2050 Initiative (TWI2050) includes a comprehensive assessment of technologies, economies and societies embodied in the SDGs ( [[#IIASA--2018|IIASA 2018]] ). The assessment addresses social dynamics, governance and sustainable development pathways within the areas of human capacity and demography, consumption and production, decarbonisation and energy, food, the biosphere and water, smart cities and digitalisation. The report concludes that the 17 SDGs are integrated and complementary and need to be addressed in unison. Studies using global IAMs that were presented in the GEO6 report ( [[#United%20Nations%20Environment%20Programme--2019|United Nations Environment Programme 2019]] , Chapter 22) concluded that transitions to low-carbon pathways will require a broad portfolio of measures, including a mixture of technological improvements, lifestyle changes and localised solutions. The many different challenges require dedicated measures to improve access to, for example, food, water and energy, while at the same time reducing the pressure on environmental resources and ecosystems. A key contribution may be a redistribution of access to resources, where both physical access and affordability play a role. The IAMs cover large countries and regions, and localised solutions are not properly addressed in the modelling results. This implies that, for example, trade-offs between energy access and affordability are not fully represented in aggregate modelling results.

There are also several country-level studies for deep emissions reductions (see [[IPCC:Wg3:Chapter:Chapter-4|Chapter 4]] for an overview of the results). The studies find significant impacts of mitigation policies at the sectoral level, reflecting the fact that the sectoral scope does not allow for as much flexibility in mitigation measures despite macroeconomic impacts being assessed to be small (Executive Summary in Chapter 4). Another key lesson is that the detailed design of mitigation policies is critical for the distributional impacts (Executive Summary in Chapter 4). The potential mitigation measures, the potential economic growth, the political priorities and so forth are different among nations, and there may be several emissions-reduction transition pathways to long-term goals among nations (Figure 4.2).

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