2. Catalysing change through a sustainable electricity sector
This chapter analyses the electricity sector through a well-being lens. The first part discusses a number of policy priorities beyond the traditional concerns of reliability, affordability and decarbonisation – the so-called “energy trilemma”. It highlights the importance of considering different scales (plant, network and demand level) and how these scales can help governments better ensure synergies between climate and other priorities, strengthening two-way alignment. For instance, it shows how activating the role played by the demand side can enhance both affordability and system flexibility, allowing a higher share of variable renewable energy resources to be integrated in the generation mix. The second part of the chapter proposes a set of indicators enabling policy makers to track progress towards multiple priorities, assessing the synergies and trade-offs between climate action and other well-being priorities.
Electricity is at the heart of human well-being since the energy it delivers supports a broad range of basic services, economic infrastructure and activities, and facilitates education and gender equality among other things. Despite improvements in energy efficiency, global electricity demand grew by 115% between 1990 and 2016, much faster than the increase in population over the same period (41%). This trend will likely continue due to economic growth and increasing access and electrification of end-uses.
Current electricity generation, notably through the combustion of fossil fuels, is the single largest contributor to global GHG emissions, pollutes the air, damaging public health and ecosystems, all of which harms current and future well-being. While decarbonisation of the electricity sector has become a policy priority, the sector is still off-track to meet global mitigation goals. Worryingly, electricity-related GHG emissions rose by 2.5% in 2018, due to large increases in gas and coal generation, a problematic trend for decarbonising industry, transport and housing that increasingly rely on a supply of low-carbon electricity. Coal-fired electricity accounted for 30% of global energy-related CO2 emissions in 2018, mostly due to relatively young plants in Asia that could lock-in high-levels of emissions for the next 30 years.
Exploiting synergies and addressing trade-offs between climate and other priorities is an opportunity to accelerate decarbonisation while bringing other well-being benefits. For example, phasing out coal reduces GHG emissions and air pollution, creating immediate health benefits that can increase the social and political acceptability of more stringent climate action. Coal phase out, however, impacts employment opportunities and people’s livelihoods, creating difficulties for some communities. These difficulties can be addressed by appropriate policy design to mitigate negative impacts on particular population groups.
Adopting a well-being lens entails incorporating objectives beyond the energy trilemma (affordability, reliability, decarbonisation), and looking at the entire power system. Setting priorities like ensuring public health and safety, sustainably managing natural resources, preserving ecosystems and providing high-quality employment opportunities is central for shifting to a sustainable energy sector. Delivering these multiple priorities requires looking beyond the plant level, examining the network infrastructure and the demand side (e.g. households, industry). For example, activating and transforming the demand side through energy efficiency improvements (e.g. using efficient household appliances and electric motors in industry) and active demand management can reduce energy bills for households and industrial consumers while enhancing system flexibility and improving the integration of variable renewable energy sources such as wind and solar photovoltaic into the grid.
Systematically monitoring all areas of electricity-related well-being is essential to supporting this shift. For example, indicators allowing to better identify households at risk of energy poverty will enable policy makers to better target income transfers and allocate infrastructure costs. These and other indicators that monitor other priorities (e.g. health, safety, ecosystems) are needed for identifying synergies and trade-offs. In addition, complementing current measurement of production-based carbon intensity with consumption-based metrics, and monitoring the extent to which governments are unlocking the potential of demand management, provides better information for setting priorities. Policy packages will need to include carbon pricing, renewable energy support and unabated coal phase-out (plant level), network planning and electricity market design (network level), and the creation of adequate regulatory conditions to activate the demand-side.
This report argues that a change in perspective – i.e. applying a well-being lens to policy making – is central to assessing the synergies and trade-offs of climate policies, and thus to achieving a two-way alignment1 between climate and broader well-being objectives. Adopting a well-being lens implies that:
Societal goals are defined in terms of well-being outcomes (including limiting climate change through mitigation) and are systematically reflected in decision-making across the economy.
The decisions taken consider multiple well-being objectives, rather than focusing on solving a single objective or a very narrow range of objectives.
The relations between the different sectors and elements of the system in which a policy intervenes are well understood.
The present chapter applies a well-being lens to the electricity sector. It derives a number of policy priorities characterising a sustainable electricity sector and proposes a set of indicators that can be used to track progress and guide policies, in line with the policy goals proposed. As such, it provides a general framework centred on well-being, used to guide policies.
Electricity, a hugely versatile form of energy, affects human well-being and the Sustainable Development Goals (SDGs) in various ways. On the one hand, electricity allows lighting and heating buildings, which increases the comfort, health and safety of residents (SDG 3); facilitates education (SDG 4) and gender equality (SDG 5); and supports a broad range of basic services, as well as economic infrastructure and activities (e.g. SDGs 6, 7, 8, 9 and 11). On the other hand, electricity generation through the combustion of fossil fuels is a major contributor to global greenhouse gas (GHG) emissions and climate change, with negative impacts on current and future well-being, public health (SDG 3), marine and terrestrial biodiversity (SDGs 14 and 15) and more generally, sustainable development (IPCC, 2014[1]).
Electricity has helped spur economic development in modern societies and is becoming the fuel of choice for many end uses. As a result, the main objective of the electricity sector has been to provide affordable and reliable electricity for all (SDG 7). With the advent of climate change on the political agenda, climate change mitigation is increasingly featured on the list of policy priorities, culminating in the so-called energy trilemma, i.e. the pursuit of reliable, affordable and low-carbon energy.
While the dimensions of the energy trilemma remain key, electricity generation affects human well-being in many other ways, adding complexity to managing the low-carbon transition. For instance, fossil-fuel power plants are a major contributor to air, water, and soil pollution, causing serious impacts on public health, ecosystems and biodiversity. Some low-carbon technologies – notably large hydro dams – often require displacing communities. They cause deforestation and landscape degradation, negatively affecting biodiversity and ecosystems, and compromising current and future human well-being (McCully, 2001[2]). The impacts associated with low-emission technologies such as nuclear energy and large-scale deployment of carbon capture and storage (CCS), include issues related to safety, long-term storage and leakage risks for CCS (IPCC, 2014[3]), which remain a concern and affect public acceptability in some countries. Variable renewable energy (VRE) sources (such as wind power plants) also have some adverse impacts, e.g. on migratory birds and bats (Tabassum-Abbasi et al., 2014[4]).
Many countries are already shifting from fossil-based power generation to renewable energy, but the pace of change in decarbonising the sector is too slow to be consistent with global climate change mitigation goals (IEA, 2019[5]). In fact, stringent mitigation goals require not only rapid decarbonisation of electricity, but also increasing electrification of end-use sectors (IPCC, 2014[1]), (IPCC, 2018[6]). Creating two-way alignment between climate change and other well-being goals by exploiting the synergies and effectively addressing the trade-offs can accelerate the pace of decarbonisation while delivering multiple well-being objectives and enabling the shift towards a sustainable electricity sector that accounts for the impacts mentioned above.
Looking at the entire power system through a well-being lens reveals an even larger number of impacts on current and future well-being (including climate change), increasing the levers of action and offering wider opportunities to deliver multiple well-being goals. Demand reduction through improvements in energy efficiency can lower the energy bill of households and industrial customers while decreasing the investment needs in generation and network capacity, with positive impacts on ecosystems and finite natural resources (land, materials) (IEA, 2018[7]). Equally important, exploiting the potential of distributed energy resources (e.g. demand response, “behind-the-meter generation”) and increased sector coupling (e.g. heat pumps, electric vehicles) can enhance flexibility, facilitating the integration of high shares of VRE resources (IEA, 2017[8]).
Section 2.2 discusses how applying a well-being lens to the electricity sector reveals the multiple impacts of electricity on well-being and is fundamental to accelerating the shift towards a sustainable electricity sector. It argues that systematic consideration of multiple well-being objectives is necessary to create two-way alignment between climate change mitigation and other well-being goals, exploiting the many synergies while effectively anticipating and addressing the existing and potential trade-offs between climate change mitigation and other priorities. Such synergies enhance the political and social acceptability of climate action.
Section 2.3 contends that the shift in perspective needs to be supported by a set of indicators that systematically reveal the electricity sector’s various impacts on human well-being, and facilitate the design of policies supporting two-way alignment. It proposes a number of indicators for monitoring, evaluating and refining these policies as necessary. The indicators presented are not exhaustive, but provide best-practice examples for tracking progress towards a sustainable electricity sector.
Electricity is fast becoming the energy form of choice. Electricity powers digital technologies, communication infrastructure and industrial operations, laying the foundation for economic prosperity (SDG 8), modern infrastructure and industry (SDG 9), and sustainable cities (SDG 11). In developing and emerging countries, ensuring access to electricity (SDG 7) correlates positively with reduced levels of poverty (SDG 1), improved public health (SDG 3), better educational attainment (SDG 4) and more gender equality (SDG 5).
Lack of access to electricity or supply disruptions have very negative impacts on human well-being. Despite significant progress in recent years, the world is not on track to provide universal access by 2030 (IEA et al., 2019[9]): 840 million people (mostly located in Sub-Saharan Africa) still lacked access to electricity in 2017. Even if households have physical access, some may be excluded from electricity consumption owing to fuel poverty, which may force households to reduce space heating or cooling to levels that reduce comfort and therefore well-being. Finally, electricity outages – though rarely observed in developed countries – are associated with large losses in production, damage to equipment and negative impacts on well-being, including risks to health and safety, and loss of leisure time (Linares and Rey, 2013[10]). For example, the July-August 2011 blackout in Cyprus produced a welfare loss of up to EUR 1 billion, equivalent to roughly 4% of the country’s gross domestic product (GDP) (Zachariadis and Poullikkas, 2012[11]); this was the largest welfare loss in Europe over the last ten years (European Commission, 2018[12]). Most governments have long acknowledged the importance of electricity consumption in promoting human well-being and economic development. Hence, providing access to affordable and reliable electricity has always ranked among governments’ top policy priorities.
Nevertheless, electricity generation is associated with significant negative impacts on a number of well-being dimensions: fossil-fuel power plants – especially unabated coal plants – are major contributors to GHG emissions and climate change. Electricity generation is the single largest contributor to carbon dioxide (CO2) emissions, accounting for 38% of global energy-related CO2 emissions in 2018 (IEA, 2019[13]). From 1990 to 2018, global CO2 emissions from electricity nearly doubled, from 6.7 GtCO2eq to 13 GtCO2eq (IEA, 2019[13]). Coal-fired power plants still account for 60% of global installed generation; 200 GW of coal capacity are currently under construction, which run the risk of becoming stranded assets (Mirabile and Calder, 2018[14]).
Although many governments increasingly consider the mitigation of electricity-related GHG emissions as a policy priority, current policies and recent trends are not sufficient to reach global mitigation goals (IEA, 2019[5]). Acknowledging climate change mitigation as a policy priority alongside electricity affordability and reliability resulted in the so-called energy trilemma. However, electricity generation is associated with a number of other detrimental impacts on human well-being, briefly summarised as follows:
Fossil power plants remain a major contributor to air pollution – e.g. sulphur oxide (SOx), nitrogen oxide (NOx) and particulate matter – with serious impacts on public health (OECD/IEA, 2016[15]). Despite important progress in reducing air pollution from the power sector in recent years,2 air pollution remains a serious problem: in 2013, about 22 900 premature deaths in the European Union (EU) could be attributed to currently operational coal plants (CAN et al., 2016[16]), almost equivalent to the number of fatalities in road-traffic accidents (26 000). Coal power plants are also a major source of mercury emissions (EPA, 2016[17]). When airborne mercury enters the water cycle, it interacts with bacteria that convert it into its highly toxic form, methylmercury, which negatively affects aquatic ecosystems and animals, endangering fish-eating birds and mammals, as well as their predators. (EPA, 1997[18]). Finally, thermal power plants are also a major source of toxic waste, which can negatively affect the local environment if not properly stored (National Research Council, 2010[19]).
Some generation technologies consume large amounts of finite natural resources (land, materials, water) and impact ecosystems. Coal mining drastically alters the landscape: it has negative impacts on ecosystems through deforestation and habitat destruction, and can pollute the groundwater through leaks from coal waste sites or acid mine drainage – the flow of acidic water into nearby rivers and streams (Epstein et al., 2011[20]). Thermal power plants’ energy-conversion efficiency has not improved significantly in the last decades (Ayres, Turton and Casten, 2007[21]), as they continue to reject large amounts of waste heat using water as a coolant; the subsequent release of hotter water has negative impacts on ecosystems and biodiversity (Goel, 2006[22]).
Low-carbon technologies can also have negative impacts on public health, ecosystems and biodiversity, while consuming natural resources. Nuclear accidents such as Chernobyl, for which the cumulative death toll related to cancer is estimated at between 4 000 (IAEA, WHO and UN, 2005[23]) and 16 000 (Cardis et al., 2006[24]), and Fukushima-Daichi, have had a range of serious physiological, developmental, morphological and behavioural impacts for terrestrial and marine plants and animals, owing to their exposure to radioactivity (Steinhauser, Brandl and Johnson, 2014[25]). CCS continues to impact on ecosystems through upstream mining activities and water use of thermal power plants (IPCC, 2014[3]). Although large hydro dams interfere with the surrounding ecosystems, many large hydro projects are currently constructed or planned in the world’s most biodiverse river basins (Amazon, Congo, Mekong) (Winemiller et al., 2016[26]).3 Large-scale bioenergy, as foreseen by scenarios compatible with limiting climate change to 1.5 degrees Celsius (°C), can put significant pressure not only on ecosystems and biodiversity, but also on available land and food production (IPCC, 2018[6]). Other renewables (i.e. solar photovoltaic [PV], wind, tidal) can have negative impacts on ecosystems and biodiversity through loss or fragmentation of habitats. These impacts can be addressed to varying degrees by appropriate policy design, so that low-carbon generation does not necessarily come at the expense of other well-being goals (Gasparatos et al., 2017[27]). Solar PV and modern wind turbines require a range of precious and rare earth metals (e.g. silver and indium), whose scarcity may represent bottlenecks in the future (Grandell et al., 2016[28]).
The electricity sector also plays a large role as an employer. The transition towards an electricity sector with high shares of renewables has important implications for employment opportunities, local communities, and people’s livelihoods and well-being. While the transition is likely to have a positive impact on gross employment, it may create difficulties for some regions and communities, notably those that rely on coal extraction (OECD, 2017[29]). These local employment effects put major pressure on the regional development and well-being of the affected communities, and need to be managed carefully. The transition also involves changes in the quality of jobs, e.g. as the number of mine workers decreases and employment in renewables increases.
While managing the dimensions of the energy trilemma is already complex, viewing the electricity sector through a well-being lens shows that it includes dimensions beyond those contained in the energy trilemma. A sustainable electricity sector provides affordable and reliable electricity for all, while: i) limiting climate change; ii) ensuring public health and safety; iii) sustainably managing natural resources; and iv) providing high-quality employment opportunities.
A sustainable electricity sector provides affordable and reliable electricity for all, while: i) limiting climate change; ii) ensuring public health and safety; iii) sustainably managing natural resources; and iv) providing high-quality employment opportunities.
Assessing the generation technologies in terms of well-being requires adopting a full-cost accounting approach, which incorporates all relevant external costs, risks and benefits to determine each country’s low-carbon generation portfolio compatible with sustainable development. This assessment clearly needs to go beyond the plant level, examining the network infrastructure and the demand side to get a comprehensive picture of the social costs of electricity.
In addition, while the levelised costs of electricity of VREs are already on par with fossil-fuel power plants in many locations, large-scale deployment of VREs in addition to distributed generation requires higher levels of system flexibility (Jairaj et al., 2018[30]). Integrating increasing shares of intermittent VREs cost-effectively while maintaining high levels of reliability requires implementing a set of measures, including operational improvements to the existing fleet, advanced VRE plant design and investments in additional infrastructure (transmission lines, back-up and storage capacity) (IEA, 2017[8]) (2018[31]). But a key measure to provide flexibility also consists in activating the demand side, discussed in the next section.
2.2.1. Broadening the perspective: Activating the demand side
A key element of the well-being lens is to study the entire power system, including the generation fleet, the network infrastructure and the demand side (Figure 2.1). Looking at these scales provides a comprehensive picture of the electricity sector and increases the levers of action to meet multiple well-being goals. For instance, demand reduction (through improvements in energy efficiency) and demand response (by shifting load over the course of a day) contribute to well-being and sustainable development in multiple ways. They improve affordability by reducing not only operational costs (e.g. avoided fuel costs), but also the need for capital expenditures in new generation and network capacity. This, in turn, reduces the energy bill of private and industrial consumers, while lowering the pressure on ecosystems and natural resources. Importantly, demand response also enhances the flexibility of the electricity system, allowing for better integration of VREs.
Demand reduction in end-use sectors is critical to curb electricity demand and GHG emissions while delivering other well-being benefits (IEA, 2018[32]). Despite improvements in energy efficiency, global electricity demand grew by 115% between 1990 and 2016 – more than double the rate of growth (52%) in total final consumption of all energy over the same period (IEA, 2019[33]). This trend is likely to continue in the next decades owing to economic growth and the increasing electrification of end-use sectors (IEA, 2017[34]). While energy efficiency will remain key, there exists a paradigm shift – triggered by the penetration of distributed energy resources, combined with digital technologies and increased sector coupling – towards activating the demand side.
While energy efficiency will remain key, there exists a paradigm shift – triggered by the penetration of distributed energy resources, combined with digital technologies and increased sector coupling – towards activating the demand side.
Distributed energy resources encompass a large variety of local energy sources, including small generation units (small hydro, rooftop solar), energy storage, demand response and electric vehicles. Generation “behind the meter” is blurring the traditional boundary between electricity generation and consumption: self-producing customers can now play an active role in the power system, transforming the traditional power system from a unidirectional centralised system towards a bidirectional decentralised system. Similarly, demand response has been limited to large industrial producers, but emerging digital technologies offer great potential for demand response in other end-use sectors, through smart meters and smart appliances (IEA, 2017[35]). In addition, smart-charging and vehicle-to-grid technology, and their aggregated deployment for system services, allows using electric vehicles (EVs) as electric storage (ITF, 2019[36]). Demand and storage aggregators, as well as virtual power plants that aggregate the production of many small-scale producers, can actively participate in electricity markets, enhancing system flexibility cost-effectively.
Sector coupling – i.e. integrating end-use sectors in the electricity system, either by electrifying end uses or producing feedstock from electricity (power-to-gas, power-to-heat) – further increases the levers of system flexibility. While electrifying end uses is key to ensuring a low-carbon pathway for the whole economy, some end uses (e.g. heat pumps for space heating and electric vehicles) can also contribute to system flexibility (IEA, 2018[7]). Using low-carbon electricity to produce synthetic fuels (e.g. hydrogen and ammonia) can also play an important role in decarbonising industry (chemicals) or transport (e.g. fuel-cell electric vehicles) (IEA, 2019[37]). Moreover, power-to-gas technology can enhance the system flexibility, potentially balancing seasonal disparities between electricity production and consumption (ENTSO-E and ENTSOG, 2018[38]).
2.2.2. A two-way alignment between climate change mitigation and other policy priorities is necessary to accelerate the shift towards a sustainable electricity sector
Adopting a holistic approach and tackling climate change mitigation in parallel with other policy priorities can increase synergies and reduce trade-offs between climate action and broader well-being goals. Such an approach can produce important short-term benefits that can enhance social and political acceptability, thereby accelerating the decarbonisation of the electricity sector while ensuring it does not come at the expense of other well-being priorities. Table 2.1 illustrates the benefits of analysing climate change mitigation through a well-being lens by systematically incorporating the policy priorities comprising a sustainable electricity sector.
As noted in Chapter 1, a change in the measurement system is key to implementing a change in perspective for policy making. This section proposes a set of indicators that policy makers can use to track progress towards a sustainable electricity sector, guide policy decisions and assess two-way alignment. Several initiatives that have developed indicators to track progress towards a sustainable electricity sector already exist ( (IAEA, 2005[39]), (ESMAP, 2018[40]), (World Economic Council, 2019[41])), but tend to focus on specific issues related to sustainability (e.g. electricity access, the energy trilemma). The SDGs and the OECD Framework for Measuring Well-being and Progress (henceforth the OECD well-being framework) establish a number of well-being priorities and related indicators at the level of the whole economy. Still, there exists substantial overlap between the policy priorities identified in the previous section and the goals established in the OECD well-being framework and the SDGs (Table 2.2). This section is ordered along the policy priorities identified in Section 2.2. Summary tables at the end of each policy priority offer a recap of the indicators proposed, showing the link to the indicators used for specific SDG targets and the well-being domains of the OECD well-being framework.
The list of indicators presented here is not exhaustive. Rather, it aims to provide best-practice examples that support governments in tracking progress towards a sustainable electricity sector. It also intends to stimulate informed discussion, as well as reveal data limitations and potential data enhancements. Systematically using these indicators as criteria guiding decisions enables governments to drive the change towards a sustainable electricity sector. Importantly, policy makers should look at these indicators jointly, instead of adopting a “silo” approach that examines isolated dimensions.
2.3.1. Limiting climate change
The indicators used to track climate change mitigation in the electricity sector are relatively straightforward. They include GHG emissions of electricity production in absolute terms, carbon intensity, and the share of electricity from low-carbon or renewables in total generation. The indicators for SDG 13 focus on policies and the indicator for SDG 7.2 reflects the share of renewables in total final energy consumption, whereas the OECD well-being framework uses GHG emissions from domestic production and CO2 emissions from domestic consumption (Table 2.3). All of these indicators are aggregated over all the sectors and hence do not provide specific information on the electricity sector. This section briefly reviews widely applied high-level indicators and proposes two new indicators: i) consumption-based carbon intensity, which informs customers on the carbon footprint of electricity consumption and can complement production-based carbon intensity; and ii) marginal carbon intensity (see below), which informs on the impact of demand response on emissions when shifting load from one hour of the day to another. It also provides some analysis on “intermediary” indicators that could be useful for measuring and tracking the extent to which governments are activating demand, i.e. unlocking the potential of demand reduction and response. As discussed in section 2.2, activating the demand is an important lever that governments can use to improve the flexibility of the electricity system, allowing for better integration of VREs, while also contributing to other objectives discussed in later sections, such as affordability and the conservation of ecosystems and natural resources.
Aggregated GHG emissions is the most suitable indicator, as it determines the extent of climate change (Chapter 1). The vast majority of electricity-related GHG emissions stem from CO2 emissions caused by the combustion of fossil fuels, but other sources of GHG emissions exist, including methane and NOx emissions from combustion, as well as fugitive emissions from gas leaks (EPA, 2019[42]), or methane emissions from the artificial reservoirs of dams (Deemer et al., 2016[43]). This section focuses on CO2 emissions from combustion.
The (production-based) carbon intensity of electricity supply condenses the carbon footprint of the current electricity generation mix into one number. Complementing the carbon intensity with other indicators helps identify the various channels affecting intensity. This includes the generation share used by technology to track deployment of low-carbon technologies and identify fuel switches (e.g. from coal to natural gas), as well as the carbon intensity of each technology used to monitor efficiency improvements in the current fleet of power plants. In addition, carbon intensity only includes emissions in power plants’ operating phase; it neglects life-cycle emissions, e.g. from the construction (including extraction and process of materials) and decommissioning of power plants (for more details on life-cycle assessments [LCAs], see Section 0).
Complementing the carbon intensity of production with the carbon intensity of consumption provides a more comprehensive picture of a country or region’s electricity system, and its interconnectedness with neighbouring systems. Production-based carbon intensity measures intensity based on electricity generation by the domestic power-plant fleet, as suggested by the United Nations Framework Convention on Climate Change (UNFCCC) guidelines (IPCC, 2006[44]). Consumption-based carbon intensity measures the true carbon footprint of electricity consumption, i.e. the indirect so-called “Scope 2” emissions, which refer to “the point-of-generation emissions from purchased electricity” (WRI, 2015[45]). Neither measure includes transmission and distribution losses, which in 2016 accounted for around 8% on a global average – exceeding, however, 50% in some countries (IEA, 2018[46]).4
Information on consumption-based carbon intensity becomes increasingly important as many national and subnational governments, as well as companies, launch initiatives to reduce their consumption-based GHG emissions. For example, New York City envisages a 30% reduction by 2030 relative to 2006, Copenhagen aims to become carbon-neutral by 2025, and the British retailer Tesco announced its intent to achieve a 60% reduction in emissions by 2025 relative to 2015 (IEA, 2017[47]). These pledges typically include indirect emissions from electricity consumption (C40 Cities, 2018[48]). Local governments can use this information when introducing local taxes on the carbon content of consumer electricity. Governments can also use this metric to calculate the indirect emissions from their administrations or public projects’ carbon footprint.
Calculating consumption-based carbon intensity, i.e. the intensity of production adjusted to the carbon intensity of imports and exports, requires data on: i) the electricity flows across borders, and ii) the carbon content of the electricity traded. While these data are not always publicly available, many transmission system operators provide them, sometimes even in real time, making it possible to calculate instantaneous consumption-based carbon intensity (Tranberg et al., 2018[49]). The numbers reported below follow this approach and are taken from Electricity Map (Electricity Map, 2019[50]).
Most countries have similar ranges of production and consumption carbon intensities, but some imbalances exist, particularly for smaller countries (Figure 2.2). For example, Lithuania has a production-based carbon intensity of 170 g/kWh, but this figure more than doubles to 340 g/kWh when looking at the consumption side. The reason for this discrepancy is that Lithuania imports more than half of its domestically consumed electricity from Belarus and Russia, both of which have relatively high carbon intensities. The reverse pattern holds true in Denmark, which has a relatively high share of coal and gas in the electricity generation mix, but imports large volumes of hydro and nuclear power from Norway and Sweden while exporting rather carbon-intense electricity to Germany.
Source: Authors, based on Electricity Map (https://www.electricitymap.org/?lang=en).
Information on carbon intensity patterns throughout the day can be used to assess the carbon impact of smoothing demand.5 Carbon intensity varies across the hours of the day, depending on the current electricity mix at any given point in time. If at a certain hour of the day, the weather conditions for renewables are favourable and renewable generation is high, the carbon intensity will be relatively low, and vice versa. Capturing the impact of shifting demand on CO2 emissions requires using the marginal carbon intensity instead of the average consumption-based carbon intensity. The reason is that to generate one additional unit of electricity supply, the system operator dispatches the marginal power plant, i.e. the plant that provides the additional unit to satisfy the additional demand. This plant’s carbon intensity is equal to the marginal carbon intensity.
Smoothing demand (e.g. by dynamic pricing) can have positive impacts on climate change mitigation. Figure 2.3 shows marginal carbon intensity and the net load (i.e. electricity demand less generation from VREs) throughout an average day, exemplified for Austria (a country whose load profile is representative of many other countries) in 2018. Smoothing demand would imply shifting units from the peak demand at 8h00 to the bottom at 2h00. As this illustrative graph shows, this shift would reduce emissions, as the marginal carbon intensity at peak demand is higher than the marginal carbon intensity at 02h00, resulting in 180 - 110 = 70 g savings of CO2 per kWh. An evaluation of Chicago’s Energy-Smart Pricing Plan, a dynamic pricing pilot, show positive impacts of dynamic pricing on GHG emissions (Allcott, 2011[51]). However, these result may not easily transfer to electricity systems with different power-plant fleets (Holland and Mansur, 2008[52]).
Source: Authors, based on Electricity Map.
Activating the demand side
SDG 7.3 measures energy intensity as the primary energy per unit of GDP (Table 2.4). While this indicator is straightforward as regards calculating and providing information on the energy intensity of the whole economy, it does not allow identifying the drivers behind its evolution over time. Disaggregating energy use by sector (industry, residential, transport) and activity (space heating, lightning, electric appliances for residential electricity consumption) sheds more light on the progress on energy intensity. For example, the International Energy Agency (IEA) decomposes the evolution of energy consumption into three components: aggregate activity (e.g. population in the residential sector), sectoral structure (e.g. floor-area over population) and intensity (e.g. lightning energy over floor area) (IEA, 2018[53]). This decomposition allows identifying the drivers behind electricity consumption in specific subsectors while isolating the impacts of improvements in energy efficiency.
As discussed before, activating the demand side is key to providing the flexibility needed to integrate rising shares of VREs. Electrification of end-uses is an important strategy to decarbonise end-use sectors and therefore information on the extent of electrification is critical to track the progress. Gathering this information is relatively straightforward, as most national and international databases provide information on electricity consumption as a percentage of total final energy consumption by sector (IEA, 2018[54]). Information on the share of taxes in the electricity price versus the share of taxes in other fuels can reveal barriers to electrification, e.g. when comparing the fuel costs of electric heat pumps with those of gas or oil boilers.
Measuring the flexibility of the power system to assess its capacity to integrate VREs is not straightforward. Many sources can contribute to improving flexibility: flexible power plants (e.g. gas power plants and virtual power plants), interconnections, storage and demand response. One way to assess the power system’s capacity to integrate VREs is through output-based indicators. For example, the share of curtailed renewable energy, defined as the ratio between (involuntary) curtailment of renewables and total generation of renewables, provides information on the extent to which the power system fails to integrate renewables. Applying this indicator at a geographically disaggregated level can effectively identify bottlenecks in the integration of renewables, but does not provide information on the cause of the curtailment (e.g. congestion, lack of transmission capacity, excessive supply during low load periods) (Bird, Cochran and Wang, 2014[55]).
Indicators exist to assess the demand for and supply of flexibility. Penetration indicators for renewables, such as the ratio of wind or solar PV generation over demand or the number of hours where this indicator exceeds 100%, can be used to assess the flexibility needed to ensure continuous security of supply (AEMO, 2017[56]). Indicators on the supply side assess the capacity of flexible power plants by type of technology, the storage capacity by technology, and the capacity of flexible demand (IEA, 2019[57]). These supply-side indicators provide information about the extent to which the current power system is capable of integrating excess generation of VREs.
Most currently used indicators focus on the demand response potential (BEIS, 2017[58]), because information on the real extent of demand response is more difficult to gather and sometimes not publicly available. The potential for demand response is estimated by aggregating the electricity demand of end uses that can be shifted for every hour of the year, e.g. heat pumps and air conditioners in the residential sector, electric vehicles in the transport sector and aluminium smelters in the industry sector (IEA, 2017[47]). Information on electricity market data can reveal the extent of actual participation in demand response of large industrial customers and aggregators, i.e. intermediaries specialising in aggregating demand response from individual consumers (OFGEM, 2016[59]). Some countries (e.g. Germany) track the deployment of smart metering technologies – an enabling condition for residential consumers to participate in demand response (BMWi, 2016[60]). This indicator can be complemented by indicators on other enabling technologies, including smart appliances or load control software, which allow matching demand to the needs of the overall system in real time (IEA, 2017[35]).
Information on the regulatory framework, i.e. the instruments and regulations for demand response participation in markets, including energy markets and capacity markets, can assess countries’ readiness for demand response. In the European Union, SmartEN – a business association for digital and decentralised energy solutions – reviews the regulatory framework for demand response and ranks Member States according to their market readiness along dimensions such as market access, prequalification, payments and penalties (SEDC, 2017[61]).
2.3.2. Improving or maintaining affordability
Several indicators measure the affordability of electricity or, more broadly, energy poverty. Chapter 3 discusses competitiveness indicators related to energy-intensive, trade-exposed industries. While SDG 7 does not have a dedicated indicator to measure affordability of electricity or energy, the OECD well-being framework reports household income and housing affordability, which subsumes many items, including expenditures on rents, maintenance, gas and electricity (Table 2.5).
Looking at the electricity price alone is not sufficient to assess affordability properly. Retail electricity prices over the long term tend to be uncorrelated with some indicators of energy affordability (Flues and van Dender, 2017[62]). However, short-term changes in electricity prices can impose challenges, notably for low-income households, as adjustment processes still need to materialise. At minimum, assessing affordability requires information on the following elements: household income (or even household wealth – see Chapter 4), the quantity of electricity consumed and finally the retail price, comprising energy costs (wholesale price and supply costs), network costs, and taxes and levies.
Affordability of electricity and energy poverty are multidimensional concepts that are more accurately measured by a set of indicators rather than a single indicator, in order to better understand and monitor the drivers of energy poverty (Rademaekers et al., 2016[63]). Some indicators of energy poverty also include expenditures for space heating – which is reasonable, given that space heating is mostly provided either by electricity or fuels (Chapter 4). Focusing on electricity expenditure only would result in a biased picture, in which households with electrical heating appliances appear to have higher electricity bills, although they may actually have lower energy bills.
The European Union Energy Poverty Observatory has selected several primary and secondary indicators to track energy poverty (European Commission, 2019[64]). The first two primary indicators listed below use self-reported responses to the European Statistics on Income and Living Conditions annual survey (Eurostat, 2019[65]). The two other indicators use values on expenditure from the Household Budget Surveys (Eurostat, 2019[66]). Each of these indicators is available at the country-level and disaggregated by income deciles, urbanisation density and dwelling type (e.g. apartment, detached).
Arrears on utility bills: share of (sub-) population with arrears on utility bills, based on the question, "In the last twelve months, has the household been in arrears, i.e. has been unable to pay on time due to financial difficulties for utility bills (heating, electricity, gas, water, etc.) for the main dwelling?
Inability to keep home adequately warm: share of (sub-) population not able to keep their home adequately warm, based on the question, "Can your household afford to keep its home adequately warm?"
Hidden energy poverty: share of population whose absolute energy expenditure is below half the national median
High share of energy expenditure in income: proportion of population whose share of energy expenditure in income is more than twice the national median share.
Policy makers can use these and other indicators to monitor energy poverty, and evaluate the impact of specific climate policies and energy-tax reforms on affordability ex ante. For example, Flues and van Dender (2017[62]) assess the impact of a hypothetical harmonisation of taxes on heating fuels and electricity across 20 OECD countries, using a carbon component of EUR 45/t CO2eq. While this reform increases energy-related taxes in most countries, the authors found that if one-third of the additional revenue generated by the reform was recycled through an income-tested cash transfer, the reform would enhance energy affordability in most countries, based on three selected indicators of energy poverty:
“Ten percent rule” (TPR): the household’s energy expenditure share exceeds 10%, and the household is in the bottom-three income deciles.
Relative poverty line (RPL): the household’s disposable income after energy expenditure is below the relative poverty line.
Low-income, high-cost-share (LIHCS): this indicator combines TPR and RPL.
The chosen indicators have advantages and disadvantages. All three are positively correlated with the subjective indicator on the ability to keep the home warm (Flues and van Dender, 2017[62]). TPR is a good proxy for how many households face relatively high costs for domestic energy. According to RPL, households face affordability risks if they are below the relative poverty line (60% of median income) after energy expenditures, emphasising the income and distribution dimensions of energy affordability. LIHCS is the most selective indicator on affordability risks: by combining TPR and RPL, it identifies households in the low-income group that spend a high share of their income on energy. However, the data requirements for this indicator are relatively great.
2.3.3. Ensuring reliability
Indicators on electricity reliability and security inform policy makers and regulators about the electricity system’s current performance (disruptions of electricity supply, supply shortage to satisfy demand). While neither the SDGs nor the OECD well-being framework feature reliability indicators, many regulators provide extensive assessment reports on electricity supply, indicating past trends and highlighting future risks (Reliability Panel AEMC, 2018[67]), (Department of Energy, 2017[68]). These reports typically encompass a large set of indicators that measure the multiple dimensions of reliability, revealing the performance of the electric system and the causes of interruptions in electricity supply. This section gives a very brief overview of frequently used indicators.
Focusing on the distribution and transmission network, high-level indicators include the System Average Interruption Duration Index (SAIDI) and the System Average Interruption Frequency Index (SAIFI), which measure the duration and frequency of interruptions for an average customer during a given period (Reliability Panel AEMC, 2018[67]). Both indicators help policy makers assess the state of the network, comparing reliability both across jurisdictions and across time. In most countries, system operators are required to report whether an outage was planned or unplanned, and to communicate the cause of the interruption (e.g. operating failure, overload or external reasons, such as weather conditions).
Other indicators attempt to measure the ex-ante risk of interruptions in electricity supply. These may include specific information about relevant components of the electricity system (e.g. grid extension, interconnections, reserve capacity, storage capacity), as well as indicators measuring resource adequacy, to evaluate the risk of supply shortfalls (Bundesnetzagentur and Bundeskartellamt, 2018[69]). Commonly used indicators include:
capacity margin: excess of installed generation over peak demand
de-rated capacity margin: expected excess of available generation capacity over peak demand, whereby the available generation capacity refers to the installed capacity that can be expected to be accessible within reasonable timeframes
loss of load expectation (LOLE): the (statistically expected) number of hours per year in which supply will not meet demand over the long term, based on a probabilistic approach
expected energy unserved (EEU): expected amount of electricity not supplied to the consumer.
Peak demand affects both types of capacity margins. Policies and regulations encouraging demand response to flatten peak demand will directly translate into the indicators signalling lower risk of load curtailment. Compared to the capacity margin, the de-rated capacity margin explicitly accounts for expected intermittency of the generation fleet that provides capacity only under certain weather conditions (Ofgem, 2011[70]). Many European countries and US markets use LOLE and EEU instead of the de-rated capacity margin as a reliability indicator: although more demanding in terms of methodology and data, their probabilistic approach better captures the impact of increasing penetration of VREs on the security of electricity supply (DECC, 2013[71]).
Policy makers and network operators need to know the customers’ valuation of avoided electricity supply disruptions. This valuation is used to assess the damages caused by supply disruptions or in cost-benefit analyses aiming to assess the economic benefits of improvements in electricity infrastructure (de Nooij, Koopmans and Bijvoet, 2007[72]). One commonly used indicator is the value of lost load (VoLL), which measures the maximum electricity price customers are willing to pay to avoid load curtailment (ACER, 2018[73]). Using a stated preferences approach directly links electricity reliability to the well-being of customers, who are asked to judge their perceived discomfort in monetary values. EU regulation on the internal electricity market requires EU Member States to state the VoLL for their country and update that estimate at least every five years. For EU Member States, VoLLs range between EUR 1 500/MWh for Bulgaria and EUR 22 940/MWh in the Netherlands, an order of magnitude higher than the average European wholesale price of EUR 40/MWh and EUR 60/MWh (ACER, 2018[73]). VoLL also depends on the type of customer, with commercial and industrial customers typically expressing a higher valuation for a reliable electricity supply than residential users (London Economics, 2013[74]).
Climate change is increasingly challenging the reliability of electricity supply. This calls for a resilient infrastructure: extreme weather events such as storms, forest fires and floods cause supply disruptions; reduced water availability constrains hydro power and the operation of thermal power plants; and rising sea levels affect coastal and offshore energy infrastructure (IEA, 2015[75]). Thus, national and regional risk assessments need to incorporate climate-change risks to assess the impacts on supply security. Indicators on climate risks and infrastructure resilience are currently being developed (OECD, 2019[76]).
2.3.4. Improving accessibility
Indicators measuring (physical) access to electricity typically focus on the number of grid connections in a given area. When reporting access to electricity for measuring SDG 7 (Table 2.6), the IEA focuses on the physical connection to a grid (national grid, mini grid, off-grids), relying on databases sourced by national governments, multilateral development banks and publicly available statistics (IEA, 2017[77]). Depending on data availability, electricity access is mostly inferred either from the existence of a utility pole in a town or village, or from household surveys that explicitly ask about a households’ grid connection (SE4A and ESMAP, 2015[78]).
This approach to measuring access has several limitations. First, the existence of a utility pole in a village or town does not necessarily imply that all households are connected to the grid. Second, condensing the measurement of electricity access into a binary variable neglects the multiple dimensions of the quality of access to electricity, including the availability of electricity supply (in terms of hours per day), the voltage of electricity (e.g. low or irregular voltage), and the legality and safety of the connection. Hazardous connections in homes, notably in rural areas and slums, can cause major health issues, injuries and deaths (SE4A and ESMAP, 2015[78]).
A comprehensive assessment requires a more detailed measurement of the multiple dimensions of electricity access, but gives a more accurate picture of the status quo and allows tracking progress more precisely. While the data requirements for this assessment are greater, measurement models are currently being developed and tested, including in developing and emerging economies. The World Bank’s Multi-Tier-Framework (MTF) measures the various dimensions of access (as described in the previous paragraph) and evaluates access to electricity through a range of tiers, from 0 (no access) to 5 (full access). The MTF is adaptable and scalable to each country or locality’s specificities.
Applying the MTF to a case study in India reveals large differences. For example, a Tier 1 classification implies that capacity ranges from 1 to 50 volts (sufficient for lighting and powering basic entertainment appliances), the electricity supply lasts between 4 and 8 hours, and the connection is illegal. According to the measure founded on the existence of an utility pole in a village, the overall access rate is 96%. However, the MTF surveys show that only 69% of households actually have access to the grid; among them, only 37% enjoy access with at least Tier 1 standards (Jain and Urpelainen, 2016[79]).
The data requirements for the MTF are very demanding, which can hinder its implementation. Even the less demanding simplified framework of the MTF still relies on household surveys (SE4A and ESMAP, 2015[78]). An alternative approach to measuring electricity access is to use night-light data from satellite pictures (Dugoua, Kennedy and Urpelainen, 2018[80]). Using satellite data helps track progress towards universal access without the need to conduct costly and burdensome surveys. On the downside, this low-cost method does not allow assessing the quality dimensions mentioned above. Satellite data can also be used to infer information on the existing electricity infrastructure, e.g. medium-voltage distribution lines (Facebook, 2019[81]).
2.3.5. Ensuring public health and safety
Monitoring the electricity-related sources of air, soil and water pollutants helps track progress in cleaning up electricity generation and informs policy makers on the impact of environmental policies. Data on pollution from stationary sources is well established in most OECD countries (OECD, 2000[82]). Beginning in the 1990s, most OECD countries introduced pollutant release and transfer registers (PRTRs) to monitor pollution from stationary sources, including fossil power plants. The OECD has assisted countries in implementing PRTRs and works continuously to improve the methodology (OECD, 1996[83]). The information in PRTRs is publicly available, contributing to transparency and public participation in environmental decision-making.
PRTRs can be used to monitor pollutants originating from the electricity sector. Most PRTRs encompass multiple pollutants, including GHG emissions (e.g. CO2, methane, hydrofluorocarbon), air pollutants (e.g. SOx, NOx, carbon monoxide), heavy metals (e.g. arsenic, mercury, lead), pesticides and inorganic substances (EEA, 2019[84]). For example, the Canadian PRTR, the National Pollutant Release Inventory, currently reports 324 different pollutants (Government of Canada, 2018[85]), supporting the evaluation of policies aiming to reduce the impact of power plants on pollution. Information on the various pollutants, including GHG emissions, can help identify trade-offs and synergies between climate change mitigation and other forms of pollution, enhancing two-way alignment.
LCAs are key to providing information on the aggregate environmental impact of electricity generation technologies. While PRTRs only capture emissions from the operating phase and large combustion installations, LCAs typically cover all phases of the life cycle, from cradle to grave: construction; operation; fuel provision (in the case of biomass, fossil or nuclear plants); and decommissioning (ISO, 2006[86]). While many renewables technologies do not emit pollutants during the operation phase, the construction and dismantling of the power plants causes pollution to air, soil, and water. The bulk of renewables’ environmental impact can be attributed to the extraction and procession of materials used in plant construction, e.g. energy-intensive materials such as cement and steel (Chapter 3). However, renewables still perform substantially better than state-of-the-art fossil power plants in terms of life-cycle emissions (IPCC, 2014[1]).
It is still challenging to link the sources of pollution to the respective levels of pollution because of the many non-linearities, requiring atmospheric models to understand the dispersion of air pollutants. SDG 11.6 uses annual mean levels of particulate matter as an indicator to measure pollution levels, but this conceals fluctuation throughout the year (Table 2.7). Instead, the European Environment Agency provides real-time concentrations of various air pollutants (SOx, NOx, PM) at different observation sites in Europe (EEA, 2019[87]).6 Combined with real-time data from point sources (such as PRTR) and air quality models, these data can help governments identify the origin of exceeded air quality limit values, highlight each sector’s impact on air quality and provide information on the impact of pollution-control technologies (EPA, 2011[88]).
2.3.6. Sustainably managing natural resources and preserving ecosystems
Indicators on the land use and water consumption of electricity generation technologies highlight the energy system's impact on these limited natural resources and supports policy makers in identifying environmental pressures. Information on land use is particularly important for countries with limited availability of land, while indicators on water consumption are particularly relevant for countries suffering from water scarcity. Assessing land use and water consumption requires using a life-cycle approach (see previous section). Figure 2.4 provides estimates for both indicators.
In many cases, changes in land use come with considerable losses of biodiversity and may undermine the provision of important ecosystem services, including those needed to maintain freshwater and forest resources (Foley, 2005[89]). Indicators on biodiversity in the SDGs and the OECD well-being framework focus on threatened species (e.g. the Red List Index), but do not assess the risk of extinction attributable to the electricity sector (Table 2.8). It is particularly important to measure the impact on ecosystems of deploying renewables, which will comprise the bulk of most countries’ power systems in the future (Section 2.2). Countries having started monitoring systematically the impacts of increasing deployment of renewables in order to identify and address potential trade-offs between renewables and ecosystems. Germany started monitoring the environmental impact of the energy transition, providing indicators that help track the impacts of renewable deployment on the environment and provide a quantifiable basis for policy instruments to address the adverse impacts (Bundesamt für Naturschutz, 2018[90]). As a first step, the German report monitors the geospatial deployment of renewable energy plants over time and compares it with existing data on protected ecologically sensitive areas (Eichhorn et al., 2019[91]). Moreover, the report identifies and quantifies a number of conflicts between renewables and biodiversity, including collision of bats and birds with wind power plants, and the loss and fragmentation of wildlife habitats due to utility-scale solar PV deployment.
Note: The data show minimum and maximum consumption, depending on the specific technologies used. CSP = Concentrated solar power; IGCC: Integrated gasification combined cycle; SCPC: supercritical pulverised coal.
Source: Authors, based on (Hertwich et al., 2014[92]), (Meldrum et al., 2013[93]) and (Bakken, Killingtveit and Alfredsen, 2017[94]).
Generating technologies also require a wide range of finite materials, including aluminium, copper, iron and several rare earth metals (Kleijn et al., 2011[95]). Material requirements on a per-unit base for renewables tend to be higher than for conventional power plants (Hertwich et al., 2014[92]). For example, solar PV requires 11-40 times more copper. Monitoring the availability of materials is key to identifying supply bottlenecks. While the availability of most materials is not problematic, the supply of silver, indium, tellurium or ruthenium represent potential bottlenecks for solar PV deployment in the future (Grandell et al., 2016[28]). Similarly, neodymium, praseodymium, dysprosium and terbium are critical elements used in the permanent magnets of wind turbines (Pavel et al., 2017[96]). These potential supply risks highlight the importance of both high recycling rates and a shift towards a circular economy (Chapter 3).
2.3.7. Providing high-quality employment opportunities
The aggregate number of jobs in renewables and fossil generation is an indication of the challenges and benefits associated with the transition towards a sustainable electricity sector. Jobs can be differentiated as direct and indirect positions. Indirect jobs refer to work for suppliers who provide services and intermediate goods for the energy sector. Indirect and direct employment effects are difficult to define and therefore quantify, as not all jobs can be attributed clearly (SRU, 2017[97]). For example, monitoring indirect job numbers in renewables is challenging, as renewable energy suppliers consist of a relatively large variety of firms, most of which also offer other services besides renewables. Distinguishing between direct jobs (working for the mining or power company) and indirect jobs (suppliers) for fossil-fuel companies is, however, easier.
Gross employment growth can be an important driver of continued public support for the transition towards a sustainable electricity sector. The German monitoring report, a comprehensive monitoring system of the energy transition (Box 2.1), explicitly reports employment figures. Figure 2.5 shows the development of employment in fossil fuel and renewables in Germany from 2000 to 2016. The employment figures include both direct jobs and indirect jobs for both sources. Aggregate employment in the electricity sector increased from 554 000 in 2000 to 690 000 in 2016, but has been falling in recent years. The jobs lost in the conventional sector have been outweighed by job creation in the renewables sector, which accounted for almost 50% in 2016. Remaining employment in the fossil-based sector is mostly within the lignite sector, owing to the more labour-intensive mining industry.
Source: Authors, based on (GWS, DLR and DIW, 2018[98]).
The German monitoring report provides a best-practice example of establishing a measurement system that helps policy makers track multiple policy objectives related to the German Energiewende (energy transition). In its energy concept of 2010, the German government set out a number of climate and energy-related targets for the medium term (e.g. a 40% reduction of energy-related GHG emissions by 2020 and 10% improvement in energy efficiency by 10% by 2020).
The monitoring report analyses and condenses available data into appropriate indicators on energy-related, environmental and socio-economic dimensions. While the first part of the report describes the targets established in the energy concept, the second part monitors environmental and socio-economic dimensions beyond the quantitative targets of the energy transition, establishing a multidimensional measurement system (Table 2.9).
Looking at the aggregate employment figures shows limited reallocation of employment as a result of a transition towards a sustainable electricity sector relative to historic norms (OECD, 2017[29]). However, aggregate numbers conceal the impact of the transition to a sustainable electricity sector at the local level. Regionally disaggregated employment figures exist. For example, the US Energy and Employment Report provides regionally disaggregated data of employment numbers by technology and occupation group on a county level (NASEO and EFI, 2019[99]). This helps identify those counties (and individuals) that are negatively affected by the transition and support governments in better targeting the losers in the transition, so that no one is left behind. In addition, regionally disaggregated data also allows pinpointing the counties that benefited the most, possibly encouraging more stringent climate action at the level of the subnational government.
Assessing the quality of employment needs to account for multiple dimensions. The indicators for SDG 8.5 and SDG 8.8 include average hourly earnings and the frequency of injuries as proxies for a safe working environment (Table 2.10). The OECD Job Quality Framework proposes three dimensions of job quality: earnings quality, quality of the working environment (health and safety conditions), and labour-market security (Cazes, Hijzen and Saint-Martin, 2015[100]). The performance of fossil and renewable jobs in the electricity sector along these criteria differs among countries (notably between OECD and developing/emerging countries), reflecting national circumstances (including trade unions, resource endowment, maturity of the renewable energy sector and workforce education). Information on these criteria informs governments about the synergies and trade-offs associated with the transition towards a sustainable electricity sector, notably employee safety and health.
This chapter argued that applying a well-being lens to the electricity sector reveals a number of well-being priorities beyond the traditional energy trilemma (reliability, affordability and decarbonisation), all of which make up a sustainable electricity sector. It also emphasised the importance of considering different scales (plant, network and demand) that increase the levers of action to create a two-way alignment by exploiting the synergies between climate action and other well-being priorities. The second part of the chapter proposed a set of indicators that enable policy makers to track progress towards a sustainable electricity sector, guide policy decisions, and assess the synergies and trade-offs between climate action and other well-being priorities.
References
[73] ACER (2018), Study on the estimation of the value of lost load of electricity supply in europe, agency for the cooperation of energy regulators, Agency for the cooperation of energy regulators (ACER), https://www.acer.europa.eu/en/Electricity/Infrastructure_and_network%20development/Infrastructure/Documents/CEPA%20study%20on%20the%20Value%20of%20Lost%20Load%20in%20the%20electricity%20supply.pdf (accessed on 26 February 2019).
[56] AEMO (2017), South Australian Renewable Energy Report, Australian Energy Market Operator, http://www.aemo.com.au (accessed on 7 June 2019).
[51] Allcott, H. (2011), “Rethinking real-time electricity pricing”, Resource and Energy Economics, Vol. 33/4, pp. 820-842, https://doi.org/10.1016/J.RESENEECO.2011.06.003.
[21] Ayres, R., H. Turton and T. Casten (2007), “Energy efficiency, sustainability and economic growth”, Energy, Vol. 32/5, pp. 634-648, https://doi.org/10.1016/j.energy.2006.06.005.
[94] Bakken, T., Å. Killingtveit and K. Alfredsen (2017), “The Water Footprint of Hydropower Production-State of the Art and Methodological Challenges”, Global Challenges, Vol. 1/5, p. 1600018, https://doi.org/10.1002/gch2.201600018.
[58] BEIS (2017), Realising the Potential of Demand-side Response to 2025: A focus on Small Energy Users, Department for Business, Energy and Industrial Strategy.
[55] Bird, L., J. Cochran and X. Wang (2014), Wind and Solar Energy Curtailment: Experience and Practices in the United States, NREL.
[60] BMWi (2016), Sechster Monitoring-Bericht zur Energiewende, http://www.bmwi.de (accessed on 30 January 2019).
[90] Bundesamt für Naturschutz (2018), Erneuerbare im naturschutzfachlichen Monitoring | Naturschutz und Energiewende, https://www.natur-und-erneuerbare.de/projektdatenbank/projekte/erneuerbare-im-naturschutzfachlichen-monitoring/ (accessed on 31 January 2019).
[69] Bundesnetzagentur and Bundeskartellamt (2018), Monitoringbericht 2018, https://www.bundesnetzagentur.de/SharedDocs/Downloads/DE/Allgemeines/Bundesnetzagentur/Publikationen/Berichte/2018/Monitoringbericht_Energie2018.pdf?__blob=publicationFile&v=3 (accessed on 5 February 2019).
[48] C40 Cities (2018), Consumption-based GHG emissions of C40 cities, http://www.c40.org/gpc (accessed on 27 February 2019).
[16] CAN et al. (2016), Europe’s dark cloud, https://env-health.org/IMG/pdf/dark_cloud-full_report_final.pdf (accessed on 28 February 2019).
[24] Cardis, E. et al. (2006), “Estimates of the cancer burden in Europe from radioactive fallout from the Chernobyl accident”, International Journal of Cancer, Vol. 119/6, pp. 1224-1235, https://doi.org/10.1002/ijc.22037.
[100] Cazes, S., A. Hijzen and A. Saint-Martin (2015), “Measuring and Assessing Job Quality: The OECD Job Quality Framework”, OECD Social, Employment and Migration Working Papers, No. 174, OECD Publishing, Paris, https://dx.doi.org/10.1787/5jrp02kjw1mr-en.
[72] de Nooij, M., C. Koopmans and C. Bijvoet (2007), “The value of supply security: The costs of power interruptions: Economic input for damage reduction and investment in networks”, Energy Economics, Vol. 29/2, pp. 277-295, https://doi.org/10.1016/J.ENECO.2006.05.022.
[71] DECC (2013), Reliability Standard Methodology, Department of Energy & Climate Change, https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/223653/emr_consultation_annex_c.pdf (accessed on 27 June 2019).
[43] Deemer, B. et al. (2016), “Greenhouse Gas Emissions from Reservoir Water Surfaces: A New Global Synthesis”, BioScience, Vol. 66/11, pp. 949-964, https://doi.org/10.1093/biosci/biw117.
[68] Department of Energy (2017), Quadrennial energy review: Transforming the nations’s electricity system, https://www.energy.gov/sites/prod/files/2017/01/f34/QER%20Transforming%20the%20Nations%20Electricity%20System%20Full%20Report.pdf (accessed on 5 February 2019).
[80] Dugoua, E., R. Kennedy and J. Urpelainen (2018), “Satellite data for the social sciences: measuring rural electrification with night-time lights”, International Journal of Remote Sensing, Vol. 39/9, pp. 2690-2701, https://doi.org/10.1080/01431161.2017.1420936.
[84] EEA (2019), About E-PRTR, https://prtr.eea.europa.eu/#/static?cont=about (accessed on 21 February 2019).
[87] EEA (2019), Up-to-date air quality data, https://www.eea.europa.eu/data-and-maps/explore-interactive-maps/up-to-date-air-quality-data (accessed on 26 June 2019).
[91] Eichhorn, M. et al. (2019), “Spatial Distribution of Wind Turbines, Photovoltaic Field Systems, Bioenergy, and River Hydro Power Plants in Germany”, Data, Vol. 4/1, p. 29, https://doi.org/10.3390/data4010029.
[50] Electricity Map (2019), electricityMap | Live CO2 emissions of electricity consumption, https://www.electricitymap.org/?page=map&solar=false&remote=true&wind=false (accessed on 14 June 2019).
[38] ENTSO-E and ENTSOG (2018), Power to Gas-A Sector Coupling Perspective, https://docstore.entsoe.eu/Documents/Publications/Position%20papers%20and%20reports/ENTSOs_position_on_Sector_Coupling_final_Madrid_Forum.pdf (accessed on 21 June 2019).
[42] EPA (2019), Inventory of U.S. Greenhouse Gas Emissions, Environmental Protection Agency, https://www.epa.gov/sites/production/files/2019-04/documents/us-ghg-inventory-2019-chapter-3-energy.pdf (accessed on 26 June 2019).
[17] EPA (2016), 2014 National Emissions Inventory, version 1: Technical Support Document, https://www.epa.gov/sites/production/files/2016-12/documents/nei2014v1_tsd.pdf (accessed on 21 February 2019).
[88] EPA (2011), Air Quality Modeling Technical Support Document: EGU Mercury Analysis, Environmental Protection Agency, https://www3.epa.gov/ttn/atw/utility/epa-454_r-11-008.pdf (accessed on 26 June 2019).
[18] EPA (1997), Mercury Study Report to Congress Volume I: Executive Summary, https://www.epa.gov/sites/production/files/2015-09/documents/volume1.pdf (accessed on 21 February 2019).
[20] Epstein, P. et al. (2011), “Full cost accounting for the life cycle of coal”, Annals of the New York Academy of Sciences, Vol. 1219/1, pp. 73-98, https://doi.org/10.1111/j.1749-6632.2010.05890.x.
[40] ESMAP (2018), Policy Matters: Regulatory Indicators For Sustainable Energy, World Bank Group.
[64] European Commission (2019), EU Energy Poverty Observatory, https://www.energypoverty.eu/ (accessed on 5 June 2019).
[12] European Commission (2018), Study on the quality of electricity market data of transmission system operators, electricity supply disruptions, and their impact on the European electricity markets, https://ec.europa.eu/energy/sites/ener/files/documents/dg_ener_electricity_market_data_-_final_report_-_22032018.pdf (accessed on 5 February 2019).
[65] Eurostat (2019), EU-Statistics on Income and Living Conditions (EU-SILC), https://ec.europa.eu/eurostat/web/income-and-living-conditions/data (accessed on 5 June 2019).
[66] Eurostat (2019), Household Budget Survey, https://ec.europa.eu/eurostat/web/microdata/household-budget-survey (accessed on 5 June 2019).
[81] Facebook (2019), Predictive model for accurate electrical grid mapping - Facebook Code, https://code.fb.com/connectivity/electrical-grid-mapping/ (accessed on 19 March 2019).
[62] Flues, F. and K. van Dender (2017), “The impact of energy taxes on the affordability of domestic energy”, OECD Taxation Working Papers, No. 30, OECD Publishing, Paris, https://dx.doi.org/10.1787/08705547-en.
[89] Foley, J. (2005), “Global Consequences of Land Use”, Science, Vol. 309/5734, pp. 570-574, https://doi.org/10.1126/science.1111772.
[27] Gasparatos, A. et al. (2017), “Renewable energy and biodiversity: Implications for transitioning to a Green Economy”, Renewable and Sustainable Energy Reviews, Vol. 70, pp. 161-184, https://doi.org/10.1016/J.RSER.2016.08.030.
[22] Goel, P. (2006), Water pollution : causes, effects and control, New Age International, https://books.google.fr/books?hl=en&lr=&id=4R9CYYoiFCcC&oi=fnd&pg=PA2&dq=Water+Pollution+-+Causes,+Effects+and+Control&ots=fJgxD-_Taw&sig=3wD2VBMxl1ZBqsN34KLbEAwRh9k#v=onepage&q=Water%20Pollution%20-%20Causes%2C%20Effects%20and%20Control&f=false (accessed on 16 March 2019).
[85] Government of Canada (2018), Notice with respect to the substances in the National Pollutant Release Inventory for 2018 and 2019, Canada Gazette Part I, http://gazette.gc.ca/rp-pr/p1/2018/2018-01-20/pdf/g1-15203.pdf#page=4 (accessed on 21 February 2019).
[28] Grandell, L. et al. (2016), “Role of critical metals in the future markets of clean energy technologies”, Renewable Energy, Vol. 95, pp. 53-62, https://doi.org/10.1016/j.renene.2016.03.102.
[98] GWS, DLR and DIW (2018), Ökonomische Indikatoren des Energiesystems, https://www.bmwi.de/Redaktion/DE/Publikationen/Studien/oekonomische-indikatoren-und-energiewirtschaftliche-gesamtrechnung.html.
[92] Hertwich, E. et al. (2014), “Integrated life-cycle assessment of electricity-supply scenarios confirms global environmental benefit of low-carbon technologies”, Proceedings of the National Academy of Sciences, Vol. 112/20, pp. 6277-6282, https://doi.org/10.1073/pnas.1312753111.
[52] Holland, S. and E. Mansur (2008), “Is Real-Time Pricing Green? The Environmental Impacts of Electricity Demand Variance”, Review of Economics and Statistics, Vol. 90/3, pp. 550-561, https://doi.org/10.1162/rest.90.3.550.
[39] IAEA (2005), Energy Indicators for sustainable development: Guidelines and Methodologies, http://www.iaea.org/Publications/index.html (accessed on 21 February 2019).
[23] IAEA, WHO and UN (2005), Chernobyl’s Legacy: Health, Environmental and Socia-Economic Impacts and Recommendations to the Governments of Belarus, Russian Federation and Ukraine, https://www.who.int/ionizing_radiation/chernobyl/chernobyl_digest_report_EN.pdf (accessed on 16 March 2019).
[13] IEA (2019), Global Energy and CO2 Status Report, International Energy Agency.
[33] IEA (2019), “OECD - Electricity and heat generation”, IEA Electricity Information Statistics (database), https://dx.doi.org/10.1787/data-00457-en (accessed on 21 June 2019).
[37] IEA (2019), The Future of Hydrogen: Seizing today’s opportunities, OECD, Paris Cedex 16, https://dx.doi.org/10.1787/1e0514c4-en.
[57] IEA (2019), Tracking Clean Energy Progress: Energy Integration, https://www.iea.org/tcep/energyintegration/demandresponse/ (accessed on 19 June 2019).
[5] IEA (2019), Tracking Clean Energy Progress: Informing Energy Sector Transformations, https://www.iea.org/tcep/ (accessed on 27 February 2019).
[53] IEA (2018), Energy Efficiency 2018: Analysis and outlooks to 2040, International Energy Agency, Paris, https://dx.doi.org/10.1787/9789264024304-en.
[32] IEA (2018), Market Report Series: Energy Efficiency 2018, IEA, Paris, https://webstore.iea.org/market-report-series-energy-efficiency-2018 (accessed on 14 February 2019).
[46] IEA (2018), “OECD - Electricity/heat supply and consumption (Edition 2018)”, IEA Electricity Information Statistics (database), https://dx.doi.org/10.1787/b0a629f0-en (accessed on 27 June 2019).
[31] IEA (2018), Status of Power System Transformation 2018: Advanced Power Plant Flexibility, International Energy Agency, Paris, https://dx.doi.org/10.1787/9789264302006-en.
[7] IEA (2018), World Energy Outlook 2018, International Energy Agency, Paris, https://dx.doi.org/10.1787/weo-2018-en.
[54] IEA (2018), World Energy Statistics 2018, OECD Publishing, Paris, https://dx.doi.org/10.1787/world_energy_stats-2018-en.
[35] IEA (2017), Digitalization & Energy, International Energy Agency, Paris, https://dx.doi.org/10.1787/9789264286276-en.
[34] IEA (2017), Energy Technology Perspectives 2017: Catalysing Energy Technology Transformations, IEA, Paris, https://dx.doi.org/10.1787/energy_tech-2017-en.
[8] IEA (2017), Status of Power System Transformation 2017: System integration and local grids, International Energy Agency, Paris, https://dx.doi.org/10.1787/9789264278820-en.
[77] IEA (2017), WEO-2017 Special Report: Energy Access Outlook, http://www.iea.org/t&c/.
[47] IEA (2017), World Energy Outlook 2017, OECD Publishing, Paris/International Energy Agency, Paris, https://dx.doi.org/10.1787/weo-2017-en.
[75] IEA (2015), Making the energy sector more resilient to climate change.
[9] IEA et al. (2019), Tracking SDG 7: The Energy Progress Report, http://www.worldbank.org (accessed on 3 June 2019).
[6] IPCC (2018), Global Warming of 1.5 °C: Summary for Policy Makers, http://report.ipcc.ch/sr15/pdf/sr15_spm_final.pdf (accessed on 30 October 2018).
[1] IPCC (2014), “Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland”.
[3] IPCC (2014), “Energy Systems”, in Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, https://www.ipcc.ch/pdf/assessment-report/ar5/wg3/ipcc_wg3_ar5_chapter7.pdf.
[44] IPCC (2006), 2006 IPCC Guidelines for National Greenhouse Gas Inventories, https://www.ipcc-nggip.iges.or.jp/public/2006gl/vol2.html.
[86] ISO (2006), “14040: Environmental Management - Life Cycle assessment”.
[36] ITF (2019), ITF Transport Outlook 2019, OECD Publishing, Paris, https://dx.doi.org/10.1787/transp_outlook-en-2019-en.
[79] Jain, A. and J. Urpelainen (2016), Measuring Energy Access in India Insights from applying a multi-tier framework in cooking energy and household electricity.
[30] Jairaj, B. et al. (2018), The Future Electricity Grid: Key Questions and Considerations for Developing Countries, World Resource Institute, https://www.wri.org/sites/default/files/The_Future_Electricity_Grid.pdf (accessed on 18 September 2018).
[95] Kleijn, R. et al. (2011), “Metal requirements of low-carbon power generation”, Energy, Vol. 36/9, pp. 5640-5648, https://doi.org/10.1016/j.energy.2011.07.003.
[10] Linares, P. and L. Rey (2013), “The costs of electricity interruptions in Spain. Are we sending the right signals?”, Energy Policy, Vol. 61, pp. 751-760, https://doi.org/10.1016/J.ENPOL.2013.05.083.
[74] London Economics (2013), The Value of Lost Load (VoLL) for Electricity in Great Britain.
[2] McCully, P. (2001), Silenced rivers : the ecology and politics of large dams, Zed Books, https://www.press.uchicago.edu/ucp/books/book/distributed/S/bo20852228.html (accessed on 18 February 2019).
[93] Meldrum, J. et al. (2013), “Life cycle water use for electricity generation: a review and harmonization of literature estimates”, Environmental Research Letters, Vol. 8/1, p. 015031, https://doi.org/10.1088/1748-9326/8/1/015031.
[14] Mirabile, M. and J. Calder (2018), “Clean power for a cool planet: Electricity infrastructure plans and the Paris Agreement”, OECD Environment Working Papers, No. 140, OECD Publishing, Paris, https://dx.doi.org/10.1787/2dc84376-en.
[99] NASEO and EFI (2019), The 2019 U.S. Energy and Employment Report, https://www.usenergyjobs.org/ (accessed on 5 June 2019).
[19] National Research Council (2010), Hidden Costs of Energy: Unpriced Consequences of Energy Production and Use, National Academies Press, Washington, D.C., https://doi.org/10.17226/12794.
[76] OECD (2019), Good Governance for Critical Infrastructure Resilience, OECD Reviews of Risk Management Policies, OECD Publishing, Paris, https://dx.doi.org/10.1787/02f0e5a0-en.
[105] OECD (2017), “Air emissions by source (Edition 2017)”, OECD Environment Statistics (database), https://dx.doi.org/10.1787/f974fae7-en (accessed on 22 July 2019).
[29] OECD (2017), Investing in Climate, Investing in Growth, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264273528-en.
[82] OECD (2000), PRTR Implementation: Member Country Progress, http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?doclanguage=en&cote=env/epoc(2000)8/final (accessed on 21 February 2019).
[83] OECD (1996), Pollutant Release and transfer registries (PRTRS): Guidance Manual for Governments, OECD Publishing, Paris, http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=ocde/gd(96)32&doclanguage=en (accessed on 21 February 2019).
[15] OECD/IEA (2016), Energy and Air Pollution, OECD/IEA, https://www.iea.org/publications/freepublications/publication/WorldEnergyOutlookSpecialReport2016EnergyandAirPollution.pdf (accessed on 20 September 2018).
[59] OFGEM (2016), Aggregators-Barriers and External Impacts.
[70] Ofgem (2011), Electricity Capacity Assessment: Measuring and modelling the risk of supply shortfalls, http://www.ofgem.gov.uk (accessed on 26 February 2019).
[96] Pavel, C. et al. (2017), “Substitution strategies for reducing the use of rare earths in wind turbines”, Resources Policy, Vol. 52, pp. 349-357, https://doi.org/10.1016/j.resourpol.2017.04.010.
[63] Rademaekers, K. et al. (2016), Selecting Indicators to Measure Energy Poverty, European Commission/Trinomics , Rotterdam, https://ec.europa.eu/energy/sites/ener/files/documents/Selecting%20Indicators%20to%20Measure%20Energy%20Poverty.pdf (accessed on 20 September 2018).
[67] Reliability Panel AEMC (2018), 2017 Annual Market Performance Review, https://www.aemc.gov.au/sites/default/files/2018-03/Final%20report.pdf (accessed on 19 March 2019).
[78] SE4A and ESMAP (2015), Beyond Connections, Energy Access Redefined.
[61] SEDC (2017), Explicit Demand Response in Europe: Mapping the Markets 2017, Smart Energy Demand Coalition, http://www.smartenergydemand.eu (accessed on 7 June 2019).
[101] Sharma, T. et al. (2016), “Of pilferers and poachers: Combating electricity theft in India”, Energy Research & Social Science, Vol. 11, pp. 40-52, https://doi.org/10.1016/j.erss.2015.08.006.
[97] SRU (2017), Start coal phaseout now, https://www.umweltrat.de/SharedDocs/Downloads/EN/04_Statements/2016_2020/2017_10_statement_coal_phaseout.pdf;jsessionid=EFD43F4CD76D0DF5D337F8273C1BD8B3.2_cid284?__blob=publicationFile&v=4.
[25] Steinhauser, G., A. Brandl and T. Johnson (2014), “Comparison of the Chernobyl and Fukushima nuclear accidents: A review of the environmental impacts”, Science of The Total Environment, Vol. 470-471, pp. 800-817, https://doi.org/10.1016/J.SCITOTENV.2013.10.029.
[4] Tabassum-Abbasi, M. et al. (2014), “Wind energy: Increasing deployment, rising environmental concerns”, Renewable and Sustainable Energy Reviews, Vol. 31, pp. 270-288, https://doi.org/10.1016/j.rser.2013.11.019.
[49] Tranberg, B. et al. (2018), Real-Time Carbon Accounting Method for the European Electricity Markets, https://arxiv.org/pdf/1812.06679.pdf (accessed on 11 February 2019).
[103] Vanclay, F. (2017), “Impact Assessment and Project Appraisal Project-induced displacement and resettlement: from impoverishment risks to an opportunity for development?”, https://doi.org/10.1080/14615517.2017.1278671.
[104] Wilmsen, B., M. Webber and Y. Duan (2011), “Involuntary Rural Resettlement”, The Journal of Environment & Development, Vol. 20/4, pp. 355-380, https://doi.org/10.1177/1070496511426478.
[26] Winemiller, K. et al. (2016), “Balancing hydropower and biodiversity in the Amazon, Congo, and Mekong”, Science, Vol. 351/6269, pp. 128-129, https://doi.org/10.1126/science.aac7082.
[41] World Economic Council (2019), Energy Trilemma Index, https://trilemma.worldenergy.org/ (accessed on 21 July 2019).
[45] WRI (2015), GHG Protocol Scope 2 Guidance: An amendment to the GHG Protocol Corporate Standard, https://ghgprotocol.org/sites/default/files/standards/Scope%202%20Guidance_Final_0.pdf (accessed on 11 February 2019).
[102] Yakubu, O., N. Babu C. and O. Adjei (2018), “Electricity theft: Analysis of the underlying contributory factors in Ghana”, Energy Policy, Vol. 123, pp. 611-618, https://doi.org/10.1016/j.enpol.2018.09.019.
[11] Zachariadis, T. and A. Poullikkas (2012), “The costs of power outages: A case study from Cyprus”, Energy Policy, Vol. 51, pp. 630-641, https://doi.org/10.1016/J.ENPOL.2012.09.015.
Notes
← 1. Creating two-way alignment between climate action and the broader goals of human well-being and sustainable development means that: i) action in non-climate policy areas should support, rather than undermine, the pursuit of climate change mitigation goals; and ii) climate change mitigation will be more attractive when it also meets other important societal goals (e.g. clean air and improvements in health) that are likely to materialise over a shorter timescale.
← 2. For example, between 1990 and 2016, power-related SOX emissions in OECD countries fell by more than 75 %, whereas NOX emissions fell by almost 50% (OECD, 2017[105]).
← 3. Forced resettlements due to (coal) mining and large hydro dams can lead to significant emotional pain as displaced people lose the places to which they are attached (Vanclay, 2017[103]). For example, according to recent estimates, the Three Gorges Dam in China is estimated to have displaced more than one million people (Wilmsen, Webber and Duan, 2011[104]).
← 4. Transmission and distribution losses are typically measured as the residual between total electricity generation and total electricity consumption. They can be divided into technical and non-technical losses. Technical losses refer to the energy lost in the transport of electricity, which can be reduced by upgrading transmission lines or power transformers, or by improving operational practices. The major reason for non-technical losses, notably in developing and emerging economies, is power theft, which constitutes a severe problem for utilities’ financial sustainability (Sharma et al., 2016[101]). Lack of electricity access and problems of affordability are major drivers for power theft (Yakubu, Babu C. and Adjei, 2018[102]).
← 5. Similarly, electricity consumers can use this information to improve their carbon footprint by shifting demand over time. Shifting demand from a high to a low carbon-intense hour would result in CO2 emission savings.
← 6. These data can be complemented by satellite data to derive a better understanding of pollution levels at different locations. For example, NASA and the Copernicus Atmospheric Modelling Service provide air-pollution data on an hourly basis, with grid cells as small as 10 km by 10 km.