Closing the energy divide in a climate-constrained world: A focus on the buildings sector

Energy & Buildings 199 (2019) 264–274

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Closing the energy divide in a climate-constrained world: A focus on the buildings sector Aline Ribas∗, André F.P. Lucena, Roberto Schaeffer Energy Planning Program – Universidade Federal do Rio de Janeiro (COPPE/UFRJ), Centro de Tecnologia, Sala C-211, C.P. 68565, Cidade Universitária, Ilha do Fundão, 21941-972 Rio de Janeiro, RJ, Brazil

a r t i c l e

i n f o

Article history: Received 21 June 2018 Revised 9 May 2019 Accepted 25 June 2019 Available online 25 June 2019 Keywords: Energy poverty Human well-being Carbon budgets Energy decarbonization policies Buildings-related emissions

a b s t r a c t Access to modern energy services in households is central to achieving decent living standards and wellbeing. Almost 3 billion people in Sub-Saharan Africa and developing Asia still lack access to modern energy services and endure energy consumption rates equivalent to a fraction of that in developed countries. While critical for the successful achievement of several internationally agreed Sustainable Development Goals (SDGs), closing this energy divide may pose an additional pressure on the already daunting challenge of securing climate stabilization. The ways in which this divide is closed will significantly affect the development of buildings-related emissions. This paper revisits previous work on the potential conflict between efforts towards closing the energy divide and enabling the achievement of higher levels of human well-being, and those associated with climate stabilization. It estimates the additional energy needed to achieve the former in terms of final energy consumption levels in key regions, and the associated emission pathways under different climate action scenarios. It then analyses the impact that such pathways could have on estimated carbon budgets associated with fulfilling the Paris Agreement, and provides some suggestions on how this impact could be minimized in the buildings sector. © 2019 Elsevier B.V. All rights reserved.

1. Introduction Energy has played a vital role in humanity’s development and growth, as a critical factor in the provision of clean water, sanitation, healthcare, mechanical power for industries, transport, communication, and refrigerated food and medication, among others. As such, energy has long been recognized as not only essential for survival, but also as a key component to every aspect of enhanced human development and well-being [10,11,50,54]. More recently, its importance received a new level of political recognition with the adoption in 2015 by 193 countries of the 2030 Agenda for Sustainable Development [14], outlining seventeen Sustainable Development Goals (SDGs), among which a goal to ensure universal access to modern energy services by 2030 (SDG 7). Furthermore, because energy is a critical enabler of broader development, several other SDGs depend on SDG 7 s progress, including those related to poverty (SDG 1), hunger and food security (SDG 2), health and well-being (SDG 3), quality education (SDG 4), gender equality (SDG 5), clean water and sanitation (SDG 6), sustainable

∗

Corresponding author. E-mail addresses: [email protected] (A. Ribas), [email protected] (A.F.P. Lucena), [email protected] (R. Schaeffer). https://doi.org/10.1016/j.enbuild.2019.06.053 0378-7788/© 2019 Elsevier B.V. All rights reserved.

economic growth and decent work (SDG 8), and inequality within and among countries (SDG 10) [31]. Global energy poverty could be eradicated by achieving SDG 7, however much more will be needed to close the existing energy divide. In 2015, the developed world’s 1.3 billion people was responsible for about 5260 million tonnes of oil equivalent (Mtoe) of primary energy with an average of about 4.1 tonnes of oil equivalent per capita per year (toe/cap-a); meanwhile, the developing world’s 6 billion people used about 8390 Mtoe, for an average of only 1.4 toe/cap-a [15]. Moreover, within the developing world, least developed regions, like non-OECD Asia and Africa, where one third of the world population lives, saw average consumption rates of only 0.7 toe/cap-a. Such low rates of primary energy consumption on a per capita basis have been traditionally associated with energy poverty in developing countries [7,26]. Recent studies suggest that societies typically require an annual per capita primary energy consumption rate above 1.2 toe/cap-a [33] or 1.5 toe/cap-a [34] to be able to achieve decent living standards. Households in the so-called energy poor not only lack access to safe, clean fuels but rely mainly on traditional, low grade energy sources, such as animal dung, crop residues, and wood for cooking and heating [11,14,26], which cause harmful indoor air pollution and typically involve the work of women and children to

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gather fuels, who could instead be doing productive work or going to school, respectively. Such inefficient cooking fuels and technologies not only produce environmental impacts but also high levels of household air pollution with a range of health-damaging pollutants, including small soot particles that penetrate deep into the lungs. Discussions around energy poverty issues in developing countries are thus commonly associated with a range of adverse outcomes and entail a comprehensive understanding of the ways in which energy and energy services are connected to human development, quality of life, and well-being [5,28]. In contrast, understandings of energy poverty in developed countries are primarily focused on affordability of thermal comfort, thus bearing little conceptual linkage with the broader analysis of human development issues [5]. Even though the number of people without access to electricity fell from 1.7 to 1.1 billion between 20 0 0 and 2016, nearly 2.8 billion people still rely on traditional use of biomass for their energy needs for cooking [14]. Moreover, current trends suggest that: 1) about 670 million people will remain without access to electricity in 2030, primarily in sub-Saharan Africa, where the average electrification rate amounts to only 43%; and 2) over 2 billion people will remain without access to clean cooking in 2030, roughly one half in developing Asia and the other in sub-Saharan Africa [14]. Efforts required to overcome these trends and pursue higher levels of human well-being across the globe involve a substantial increase in the levels of energy consumption and access to modern energy services in households, mostly in developing Asia and the other in sub-Saharan Africa. However, at current decarbonization rates and state of knowledge and technology, the corresponding CO2 emissions associated with the additional energy consumption levels needed could compromise internationally agreed efforts towards climate stabilization, e.g. the “well below 2 °C” target agreed upon in the Paris Agreement signed in December 2015 [48]. The buildings sector will play an important role in achieving climate stabilization as it is responsible for 32 percent of the overall final energy consumption (51 percent of global electricity consumption) and 19 percent of total energy-related CO2 emissions (based on 2010 data; [23]). The ways in which universal access to modern energy services in households is delivered will significantly affect the development of buildings-related CO2 emissions. Building activity in developing countries is already intensified due to rapid economic development as well as urbanization and transitions from informal to formal housing [53]. To help better understand the dilemma at stake and where action will be most needed to mitigate building related emissions, this paper seeks to provide answers to the following pressing questions: 1) Would the additional levels of energy consumption that would be needed to achieve higher levels of human wellbeing across the globe affect existing carbon budgets associated with climate stabilization? 2) If so, which regions would be in most need of efforts to reduce the carbon impact of improvements in human wellbeing? 3) What could be done to reduce the carbon impact of improvements in human well-being in these regions, primarily in the buildings sector? In order to do so, this paper advances previous work [29] by presenting estimates of the additional final energy consumption that would be needed in key regions, as well as the corresponding associated emission pathways under different climate policy and action scenarios. It then analyses the impact that such pathways could have on estimated carbon budgets associated with fulfilling

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the Paris Agreement, and provides some suggestions on how this impact could be reduced, primarily in the buildings sector. 2. Literature overview Empirical research on the relationship between energy use and development started in the 1970s with a focus on its economic dimension (see [2]). After the concept of sustainable development made an international breakthrough with the publication of the Brundtland Report [54] broader aspects of development started to be considered. In 1990, the United Nations Development Programme (UNDP) developed the Human Development Index (HDI), the geometric mean of three normalized indices designed to measure the following key dimensions of human development: (i) longevity, measured by life expectancy at birth, (ii) educational level, measured by two variables, average literacy rate of people aged 25 or older and the expectancy of years of study, and (iii) income, measured as GDP per capita in Purchasing Power Parity. A country potentially having the highest score across all three dimensions would have an HDI value of 1.0 (UNEP, 2019). Several of the more recent studies used the HDI as a proxy for human development and well-being (e.g. [22,27,28,35,38,49]). According to their findings, slight increases in energy consumption translate into significant improvements in human development, notably in developing countries (with low or medium HDI rankings). However, after a certain saturation level, it takes significant increases in energy consumption to make any further improvements, as depicted in the regression curve in Fig. 1. An important take away from these results is the understanding that while increased energy consumption is bound to increase development, it may not necessarily be the case after societies have reached a certain level of economic development and quality of life, including adequate housing, ample food supply and clean water, decent transportation, and good indicators of health and education. Such a threshold level has been found to be equivalent to a per capita primary energy consumption rate of approximately 2.5 toe/cap-a by Martinez and Ebenhack [24] and Ugursal [49], much higher than the average consumption rate seen in the developing world, discussed in the Introduction. Nevertheless, by using the HDI as a proxy for development, these studies did not encompass the environmental dimension or the intergenerational equity principle, which are both critical for achieving sustainable development. Data shows, for instance, that progress in HDI has come at the cost of global warming [42]. The authors’ previous work [29] intended to address this shortcoming by selecting a proxy for development that encompassed all three dimensions of sustainable development. The selection process also required the indicator to have data across a relevant number of countries and over a sufficiently long time period to ensure a robust quantitative analysis and obtain broad trends. Selected among fourteen indicators, the Inclusive Wealth Index (IWI) estimates the social value of three components of the productive base of the economy separately, namely: manufactured (or produced) capital (e.g. machinery, buildings, infrastructure); natural capital (e.g. land, forests, fossil fuels and minerals); and human capital (the population’s education and skills). It then aggregates them into a weighted average using shadow prices [52]. The conceptual framework behind the IWI is provided in Arrow et al. [1]. 3. Materials and methods The assessment framework proposed in this study to help answer those questions builds on the one presented in our previous work [29]. It uses the same proxy for human development and well-being and its projections of future well-being levels. It uses

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Fig. 1. Human development index and total primary energy supply per capita for 124 countries in 2014. (1) Selected 124 countries for which data was available; (2) blue bubbles represent the pairs HDI-TPES per country and their sizes depict their population; (3) TPES is calculated as the total energy production lus imports, minus exports, minus international marine bunkers, plus or minus stock changes; (4) Vertical dotted grey line represents 1.5 toe/cap-a, the minimum energy threshold to achieve a HDI > 0.8 in the sample used. Countries above the horizontal dotted line enjoy HDI levels equal or above 0.8 and are developed (OECD members). Data sources: The Human Development Report 2015 [44] and the World Development Indicators [55]. Table 1 Emissions scenarios. Adapted from [21]. Scenario name

Corresponding scenario in LIMITS study

Scenario type

Fragmented action until

Long-term target (2100)

No-action Action as of 2020-500 Action as of 2020-450 Delayed action-500

Base RefPol-500 RefPol-450 RefPol-2030-500

Baseline Climate policy Climate Policy Climate policy

n/a 2020 2020 2030

none 500 ppm CO2 eq (3.2 W/m2 ) 450 ppm CO2 eq (2.8 W/m2 ) 500 ppm CO2 eq (3.2 W/m2 )

final energy consumption data as opposed to primary energy consumption data, which helps identify the carbon impact associated with how energy is used, as opposed to that associated with primary sources. As such, the results will help provide better energy conservation and/or efficiency policy recommendations. It then describes mathematically the relationship between the IWI and final energy consumption, as opposed to primary energy consumption data used previously. Correlations over different years provide an indication, through elasticities, of how responsive (or sensitive) human well-being has been to variations in final energy consumption over time in each region. A range of future elasticities is then applied to the IWI projections to estimate the additional final energy consumption level that would be needed in each region where improvements in human well-being are needed. Then the associated carbon emissions are obtained using CO2 emission intensities projections calculated based on projected CO2 emissions from fossil fuel combustion and industrial processes and final energy consumption levels from different climate policy and action scenarios of the integrated assessment model (IAM) Model for Energy Supply Systems Alternatives and their General Environmental Impact (MESSAGE v.4) used in the LIMITS project [21,39,40]. All scenarios used in this analysis are summarized in Table 1. Finally, all estimated ranges of associated CO2 emissions obtained are compared with regional emissions pathways based on two stabilization scenarios from the same IAM. Consistent data sets are used throughout the analysis. Unless otherwise mentioned, historical final energy data used in the analysis were obtained from the International Energy Agency [15] and other historical data were obtained from the World Development Indicators [55]. All data from the IAM MESSAGE v.4 were obtained from the IPCC’s AR5 Scenario Database [8].

3.1. Projected levels of human well-being A total of one hundred and eighteen countries for which data on IWI exist were organized into IPCC’s Region Categorization 5 (RC5), namely: OECD 90 countries (OECD90); Economies in Transition (EIT); Asia (ASIA); Middle East and Africa (MAF); and Latin America (LAM). For a complete list of countries covered in each region see Ribas et al. [29]. Their projected levels of well-being, measured in per capita IWI (pcIWI), were calculated to years 2030 and 2050, as shown in Table 2. As discussed in detail in our previous work, the projections indicate that, while ASIA and MAF represent the regions with the greatest need for improvement in human well-being, only ASIA would be on track to achieve significantly higher levels of wellbeing by 2050. Furthermore, despite much higher average levels of well-being in LAM and EIT, some countries in these regions endure very low levels of well-being, e.g. Haiti (US$ 5627), Nicaragua (US$ 17,482), Tajikistan (US$ 4627), and Kyrgyzstan (US$ 8037). By contrast, countries in the OECD90 already enjoy high levels of wellbeing, thus this region is not included in our analysis. 3.2. Correlating human well-being and energy consumption In order to estimate the additional energy consumption levels associated with the projected levels of human well-being, we examine how responsive (or sensitive) the latter has been to variations in the former during the observed time period. Previous preliminary regression runs have indicated some degree of direct correlation between energy use and growth in the different components of IWI [52]. Similarly, we apply here simple regression runs between per capita IWI and per capita final energy consumption

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Table 2 Per capita IWI across all RC5 regions in 2010 and projected to 2030 and 2050. pcIWI (in 2005 US$)

ALL REGIONS

ASIA

EIT

LAM

MAF

OECD-90

pop weighted average in 2010 median min max pop weighted average in 2030 change 2010–2030 pop weighted average in 2050 change 2010–2050

92,602 72,736 4627 758,631

20,779 17,749 5596 269,065 31,820 53% 49,861 140%

104,831 95,038 4627 247,078 117,308 12% 133,316 27%

75,944 69,748 5627 139,499 88,282 16% 113,558 50%

41,956 31,930 5721 533,044 42,705 2% 44,406 6%

405,396 433,946 75,600 758,631

Table 4 Compound annual rates of change (%) in elasticity. No elasticity change calculated for 1990–1995 in EIT since the linear model did not fit well for the 1990 data. Positive change denotes decoupling trend between final energy consumption (pcEC) and human well-being (pcIWI). Upper bound rates calculated based on prorated highest annual decrease rates observed (or lowest increase rates observed in the absence of decrease rates), to be applied in 10-year period (2011–2020). Lower bound rates calculated based on highest annual increase rates observed, prorated to be applied in 10-year period (2011–2020). ∗ Second highest rates used to avoid distortions from peaks in EIT and LAM. ASIA 1990–1995 1995–2000 2000–2005 2005–2010

Fig. 2. Correlation of human well-being measured in per capita IWI and energy consumption measured in per capita final energy consumption (1 GJ = 1 koe / 10 0 0 × 41.868) over the one hundred and eighteen countries covered here for the year 2010 (shown in log-log space). TFC is the sum of the consumption in the enduse sectors and for non-energy use. Energy used for transformation processes and for own use of the energy producing industries is excluded. Final consumption reflects for the most part deliveries to consumers. Data sources: UNU-IHDP and UNEP (2014), IEA [[15]b], and World Bank (2017). Table 3 Regression results. n = number of countries. Data for the following countries are available from 1991, thus all data refer to 1991 instead of 1990: Croatia; Kazakhstan; Kyrgyztan; Lithuania; Russian Federation; Slovenia; Tajikistan; Ukraine. Data for the following countries are available from 1992, thus all data refer to 1992 instead of 1990: Czech Republic; Slovakia.

1990 1995 2000 2005 2010

R-square

n

% world pop

a

b

p-value of b

0.65 0.81 0.82 0.81 0.80

118 118 118 118 118

93 93 93 93 93

7.18 6.69 6.56 6.45 6.31

1.05 1.20 1.22 1.23 1.26

0.000 0.000 0.000 0.000 0.000

(pcEC) (Fig. 2). By using final energy consumption data we are able to measure the emission intensities associated with the energy delivered to final consumers and, thus, avoid including energy losses due to inefficiencies in the transformation of primary energy – used in our previous work. A goodness fit exercise revealed a linear model in log-log form that best describes the relationship between the two variables. When applied to the sample countries for the years 1990, 1995, 20 0 0, 20 05, and 2010, it reveals a slightly greater increase in elasticities over time compared to our previous work using primary energy, as shown in Table 3. In other words, results here suggest that achieving higher levels of human well-being is becoming even more energy-efficient in terms of final energy consumption.

Upper bound 2011–2020 Prorated to 10 yrs Lower bound 2011–2020 Prorated to 10 yrs

EIT

LAM

MAF

−1.013 −0.478 −0.930 0.350

3.403∗ 0.832 1.008

3.167∗ −0.404 0.933 −1.926

−0.202 0.190 −0.169 0.037

−1.013 −0.507

0.832 0.416

−1.926 −0.963

−0.202 −0.101

0.350 0.175

1.008 0.504

0.933 0.466

0.190 0.095

We then test for goodness fit and apply the same model to each of the four RC5 regions where improvements in human well-being are needed. Similar to the results obtained with primary energy previously, the overall decoupling trend is not observed in ASIA and MAF. ASIA has instead seen mostly decreasing elasticities over the analysis period, possible explained by the increased share of fossil fuels in the energy mix of the region [55]. Meanwhile, MAF has not seen much variation likely also in part due to high levels of deployment of fossil fuels in this region, as well as, the higher than average population growth rate seen in this region during this period [52]. 3.3. Estimating the additional energy needed Next, a sensitivity range using observed annual rates of change is devised for each of the four regions to estimate the additional final energy consumption associated with future improvements in human well-being, as depicted in Table 4. This approach ensures that regional differences are taken into consideration while allowing for both positive and negative annual changes in future elasticities, i.e. both decoupling and coupling trends, respectively. Notably, the projected range of change for EIT is much lower than the one derived in our previous work. This is due to the fact that no coupling trend (negative change) between final energy consumption and human well-being has been observed in this region, while in the previous work a coupling trend had been observed from 2005 to 2010. By applying the prorated lower and upper bounds of changes to the elasticities to the projected levels of well-being discussed in Section 3.1, the estimated range of final energy consumption is obtained for each region. These are then calibrated to the historical

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A. Ribas, A.F.P. Lucena and R. Schaeffer / Energy & Buildings 199 (2019) 264–274 Table 5 Projected ranges of annual energy consumption and associated emissions. n = number of countries (5 outliers removed: 1 from MAF, 3 from LAM, 1 from EIT). Projected EC = estimated additional final energy consumption in Exajoules (EJ). N

% world pop

Projected EC (EJ) By 2030

Projected CO2 emissions(Gt CO2 ) By 2050

By 2030

By 2050

lower

upper

lower

upper

lower

upper

lower

upper

ASIA EIT LAM MAF

16 21 19 33

52 5 9 13

185 27 27 49

238 28 47 52

292 28 34 65

385 29 61 70

20.1 2.5 1.9 3.7

25.8 2.6 3.4 3.9

32.0 2.9 2.8 3.9

42.3 3.0 5.2 5.3

Total

89

78

287

365

418

545

28.1

35.6

42.7

55.8

data obtained from a population-weighted Total Final Consumption data from the IEA World Energy Balances 2017 [15] using population data from the World Development Indicators [55]; and scaled to aggregate values using normalized population growth projections from the IAM MESSAGE [8]. 3.4. Estimating the associated carbon emissions In order to estimate the range of associated carbon emissions for each of the four regions we apply normalized CO2 emission intensities projections to the range of additional energy for each year of the projection period, i.e. from 2011 to 2050. The CO2 emission intensities projections are based on projected CO2 emissions from fossil fuel combustion and industrial processes and final energy consumption levels (instead of primary energy data used in our previous work) from the base scenario of the IAM MESSAGE [8]. The base scenario or no climate policy baseline run reflects emissions pathways without any mitigation action [21,40]. 4. Results and discussion Results of the quantitative assessment described above are presented in this section. We estimated ranges of additional final energy consumption (projected EC) and associated carbon emissions (projected CO2 emissions) needed to enable improvements in human well-being in each of the four selected regions by 2030 and 2050, as shown in Table 5. All four regions combined would require between 287 and 365 EJ in final energy consumption and between 28.1 and 35.6 Gt CO2 in annual emissions by 2030 to increase current levels of human well-being, and between 418 and 545 EJ and between 42.7 and 55.8 Gt CO2 by 2050 to improve it even further. While the lower bound values of the projected emissions for 2030 and 2050 are very close to the results obtained previously, the upper bound ones are 13 and 11 percent lower in the present study, respectively. This is due to the fact that growth projections for final energy are higher than those for primary energy, in particular for MAF and ASIA, where electricity accounts for a large share of the final energy consumption as rising incomes in these regions are expected to lead to higher ownership of appliances and increasing demand for cooling [13,16]. ASIA continues to be by far the region that would require the highest amount of energy and CO2 emissions. While China has already shown signs of a slower economic growth trend than that seen between 1990 and 2010 (when its share of the global economic output increased from 4 to 13 percent) as well as much lower energy intensity rates, its population is not expected to stop growing before 2030 (UN, 2015). Meanwhile, India has entered a sustained period of rapid growth in energy consumption (IEA, 2015a) with its population being expected to continue growing through mid-century, exceeding China’s by 2030.

The average final energy consumption rate in ASIA would increase from 32 in 2010 to at least 44 and up to 57 GJ/capita (from 0.8 toe/cap-a to at least 1.1 and up to 1.4 toe/cap-a) by 2030. This roughly equates to increasing to at least 1.5 toe/cap-a and up to 1.9 toe/cap-a primary energy consumption rate by 2030, considering a ratio of 70 percent between final and primary consumption rates recently seen in this region ([8]; 2010 data). Thus, confirming that the region could move out of average energy poverty levels already by 2030, in contrast with findings in Lamb and Rao [22] for this region, according to which this would only take place by 2050. The results for LAM continue to indicate that the region is also on track to move further out of energy poverty levels already by 2030 with an expected increase from 41 to up to 68 GJ/capita (from 1 to up to 1.6 toe/cap-a) final energy consumption between 2010 and 2030. These equate to reaching approximately 1.3 toe/cap-a and 2.3 toe/cap-a primary energy consumption by 2030, considering a ratio of 76 percent between final and primary consumption rates recently seen in this region ([8]; 2010 data). The results for MAF also confirm that the region is not expected to advance much in terms of energy poverty before mid-century. Its final energy consumption rate would increase from 37 to up to 41 GJ/capita (from 0.9 to up to 1 toe/cap-a) in the upper range of the projections by 2050. This would equate to reaching only approximately 1.4 toe/cap-a primary energy consumption by 2050, considering a ratio of 68 percent between final and primary consumption rates recently seen in this region ([8]; 2010 data), which still falls below the decent living cap equivalent to 1.5 toe/cap-a, according to Spreng [34]. This small change would be associated with an also modest improvement in human well-being of only 6 percent in terms of per capita IWI. In terms of cumulative values, the four regions combined would require between 4823 and 5813 EJ in overall final energy consumption and between 431 and 567 GtCO2 in overall carbon emissions from 2011 to 2030 and between 11,853 and 14,873 EJ and between 1175 and 1474 GtCO2 from 2011 to 2050 to achieve higher levels of human well-being (Table 6). While the lower bound values of the projected cumulative emissions by 2030 and 2050 are very close to the results obtained in the previous study, the upper bound ones are about 10 percent lower in the present study. ASIA alone would continue to represent over 60 percent of the estimated additional energy consumption needed and up to 72 percent of the associated carbon emissions, as yielded in the results with primary energy data in our previous work. 4.1. Comparing emissions pathways The estimated ranges of final energy and associated carbon emissions are then plotted alongside two climate stabilization emissions pathways from the IAM MESSAGE, namely: Immediate action-450 and Immediate action-500, which are associated with roughly two-thirds likelihood (reasonably high chance) and

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269

Table 6 Projected ranges of cumulative energy consumption and associated emissions. n = number of countries (5 outliers removed: 1 from MAF, 3 from LAM, 1 from EIT). Projected EC = estimated additional final energy consumption in Exajoule (EJ). N

% world pop

Projected EC (EJ)

Projected CO2 emissions (GtCO2 )

2011–2030

2011–2050

2011–2030

2011–2050

lower

upper

lower

upper

lower

upper

lower

upper

ASIA EIT LAM MAF

16 21 19 33

52 5 8 13

2954 559 487 823

3603 575 764 871

7695 1108 1091 1959

9791 1145 1847 2090

295 45 31 60

390 53 54 70

837 104 82 152

1065 108 139 162

Total

89

78

4823

5813

11,853

14,873

431

567

1175

1474

Fig. 3. Change in elasticity (b values) over time. Sixteen countries were assessed in ASIA, twenty-one in EIT, nineteen in LAM, and thirty-two in MAF, after six outliers were removed, as follows: one from EIT, three from LAM, and two from MAF. The dashed line refers to the elasticities obtained in the assessment of all one hundred and eighteen countries (see Table 3). An elasticity of 1.20 means that for each 1 percent change in energy consumption (pcEC), there was a 1.20 percent change in human well-being (pcIWI).

roughly even chance of meeting the 2 °C target, respectively (see [40]). Fig. 3 depicts this comparison. The additional energy consumption levels in EIT and MAF would fall below the energy levels in the stabilization scenarios through 2050, as shown in the graphs to the left of Fig. 4. In LAM, this would be the case only in the lower levels of the range. Whereas in ASIA, not even in the lowest level of the range, i.e. in the best-case scenario. These results are in line with those from the previous study. The only difference refers to the fact that the ranges of final energy consumption and associated emissions for EIT are much narrower. Even in the highest level of the range, i.e. the worst-case scenario, the additional energy consumption in this region would still fall below those in the stabilization scenario. This is due to the fact that, differently from the previous study using primary energy, no coupling trend (negative change) between final energy consumption and human well-being was observed during the time period analyzed, as explained in Section 3.3. The associated CO2 emissions, however, would reach much higher levels than those associated with climate stabilization in all region by mid-century (see graphs of the right side of Fig. 3). Notably in ASIA, where the gap between the projected range and the stabilization scenarios could reach 37 Gt CO2 by 2050, i.e. three times above the estimated gap for the other three regions combined. We then revised our projections to reflect a future with higher decarbonization rates by using CO2 emission intensities from the three alternative climate policy scenarios from the IAM MESSAGE shown in Table 1, namely: Delayed action-500, Action as of 2020-

500, and Action as of 2020-450). The Delayed action-500 scenario refers to a scenario where regions follow existing domestic policies or mitigation actions until 2030 and adopt new policies and actions associated with even chances of meeting the 2 °C target. The Action as of 2020-450 and Action as of 2020-500 scenarios refer to scenarios where regions follow existing domestic policies or mitigation actions until 2020 and adopt new policies and actions associated with reasonably high and even chances of meeting the 2 °C target, respectively. New ranges of final energy and associated CO2 emissions were estimated for each of the three alternative climate policy scenarios. The new projected emissions were then plotted alongside the two climate stabilization pathways, as depicted in Fig. 5. The associated CO2 emissions in all three alternative scenarios would reach much lower levels than those in the base (no-policy) scenario. Nevertheless, the associated CO2 emissions in ASIA could still reach much higher levels than those associated with climate stabilization through mid-century, even if the most stringent type of climate policy and actions were taken already in 2020. Similar results would apply for LAM and MAF in the Delayed action-500 scenario. Conversely, the associated CO2 emissions in EIT would fall below levels associated with climate stabilization in all three alternative scenarios. While the results for MAF and EIT differ from those in our previous study, the overall comparison also suggests that the sooner and/or more stringent new climate policies and actions are taken, the closer the associated emissions in ASIA, LAM, and MAF would be to those associated with achieving climate stabilization. 4.2. Assessing the gaps We then compared cumulative emissions between 2011 and 2050 in all climate action scenarios with the carbon budgets associated with reasonably high chance (450 ppm budget) and even chances (500 ppm budget) of holding the increase in global temperature levels to below 2 °C in each of the four regions. According to the projections obtained, the two carbon budgets could be exceeded by as much as 818 Gt CO2 and 671 Gt CO2 in the no-action scenario, respectively, as shown in Table 7. While the gaps obtained are about 20 percent lower than those obtained in the previous study, they continue to corroborate to the conclusion that the additional energy consumption needed to achieve higher levels of human well-being across the globe would affect existing carbon budgets associated with climate stabilization (first of the three research questions), even at higher decarbonization rates. Only in the lower range of the projections and under the most stringent of the climate scenarios considered, the Action as of 2020-450, would the gap be substantially lower (32 Gt CO2 over the 450-ppm budget) or even negative (115 Gt CO2 below the 550ppm budget).

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Fig. 4. Estimated annual final energy and CO2 emissions pathways for achieving higher levels of human well-being in Asia, EIT, LAM, and MAF, compared to two stabilization scenarios from the IAM MESSAGE (Immediate action-450 and Immediate action-500), which would yield reasonably high and even chances of achieving 2 oC, respectively. The shaded area in each graph refers to the sensitivity range: using the highest observed decrease rate in elasticity to progressively decrease elasticities from 2011 to 2020 and maintaining constant thereafter as the upper bound, and the highest observed increase rate in elasticity to progressively increase elasticities from 2011 to 2020 and maintaining constant thereafter as the lower bound. Table 7 Gaps in emissions by 2050 compared to the 450 ppm and 500 ppm carbon budgets (in Gt CO2 ). Region / Scenario

No-action

Delayed action-500

Action as of 2020-500

Action as of 2020-450

at least

up to

at least

up to

at least

up to

at least

up to

450 ppm budget ASIA EIT LAM MAF All regions

421 75 48 112 655

416 29 34 40 519

644 33 92 50 818

191 1 3 15 210

350 3 37 24 414

161 (7) (7) (4) 144

312 (5) 19 3 329

71 (15) (9) (15) 32

195 (13) 14 (9) 186

500 ppm budget ASIA EIT LAM MAF All regions

527 128 57 91 803

310 14 25 24 372

539 17 82 34 671

85 (15) (6) (1) 63

245 (12) 27 8 267

56 (23) (16) (20) (3)

207 (21) 9 (13) 182

(35) (31) (19) (31) (115)

89 (29) 4 (25) 39

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Fig. 5. Estimated CO2 emissions pathways for achieving higher levels of human well-being in ASIA, EIT, LAM, and MAF in three climate action scenarios (Delayed action500, Action as of 2020-500, and Action as of 2020-450), compared to the two stabilization scenarios (Immediate action-450 and Immediate action-500), which would yield reasonably high and even chances of achieving 2 oC, respectively. The shaded area in each graph refers to the sensitivity range: using the highest observed decrease rate in elasticity to progressively decrease elasticities from 2011 to 2020 and maintaining constant thereafter as the upper bound, and the highest observed increase rate in elasticity to progressively increase elasticities from 2011 to 2020 and maintaining constant thereafter as the lower bound.

4.3. Where and what could be done in the buildings sector to help close the gaps As found in the previous study, ASIA would be the region in most need of efforts to reduce the carbon impact of the additional energy needed to achieve higher levels of human well-being (second of the three research questions). Even in the most stringent of the climate scenarios considered, the Action as of 2020-450, achieving higher levels of human well-being in ASIA would require up to 616 Gt CO2 of cumulative emissions between 2011 and 2050, exceeding the overall estimated regional carbon budget associated with a reasonably high chance of limiting the temperature increase to below 2 °C by approximately 46 percent (i.e. a gap of 195 Gt CO2 ). Notably, should MAF be able to improve its capacity to generate greater levels of produced capital and human capital, and thereby increase its well-being beyond the projected 6 percent by midcentury, it would likely see much greater gaps. As such, it would also require significant efforts to reduce the carbon impact of the additional energy associated with improvements in human wellbeing. Considering that ASIA’s urban population is expected to grow by an additional 1.4 billion by mid-century [43], a significant share of the additional energy needed to achieve higher levels of human well-being in this region of the world would come from the buildings sector. With rising incomes and a growing middle-class population, this sector is expected to undergo a major transforma-

tion in this region, in particular in cities located in China and India [46]. This transformation is expected as a result of a substantial increase in the demand for new residential floor space, new housing construction to accommodate population living in informal housing systems, demolition and replacement of buildings, and higher end-use energy demands, including space cooling and household appliances [32,41]. Therefore, in order to reduce the carbon impact associated with additional energy needs in the buildings sector as a result of improvements in human well-being in ASIA (third of the three research questions), simultaneous measures would have to be put in place to substantially increase the share of clean and lowcarbon energies (e.g. natural gas, electricity, and distributed renewables) and limit the rise of energy demand in the sector by mid-century. Such measures should include retrofitting of existing buildings with energy-saving features (e.g. computer-controlled lighting, temperature control systems, low-energy light bulbs, ceiling insulation), application of energy efficiency standards in building designs, improving thermal insulation of heating pipelines, and actively controlling demolition of buildings. Notably, the current renovation rate of just 1 percent per year of existing building stock would need to see a three-fold increase [19]. The necessary mitigation would not be achieved without a combination of effective policy instruments including building performance codes and standards, energy-efficiency labelling and certification, and public procurement regulations. In addition, new energy policies should prioritize smart-grid, district energy systems

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and community energy plans to optimize synergies between deployment of clean energy and energy efficiency gains [51]. Lastly, a strong policy push for less carbon-intensive lifestyles would further contribute to lower overall end-use energy demand. 5. Further remarks Meeting additional energy needs with cleaner energy sources in MAF would help unlock its ability to reach higher levels of human well-being measured in terms of IWI, since investments in renewable technologies could boost produced capital. Moreover, investments in such technologies would improve energy security and reduce the risk of price fluctuations for net oil-importing countries in the region (e.g. Bahrain, Israel, Jordan, Morocco, and South Africa), thereby alleviating the challenge of expanding energy access [11] without significant conflicts with climate stabilization efforts. For most countries in ASIA, and even in MAF, the potential for renewable energy surpasses present day energy demands significantly according to Climate Analytics (2016): by three-fold in Bangladesh and China, and by ten-fold in Ethiopia, for example. Even though China already ranks top in terms of installed hydropower, and wind and solar PV power generation capacity, and is the largest user of solar water heaters and geothermal heat, according to IRENA [19], it would have to increase the share of renewable energy in its energy mix from 7 percent in 2015 to 67 percent in 2050 in order to set its emissions pathway in line with a reasonably high chance of limiting the average global temperature increase to below 2 °C. Similarly, it would have to increase the share of renewable sources in its power mix from 26 percent in 2015 to 94 percent in 2050. India, in turn, would have to increase its share of modern renewables (i.e. excluding traditional use of bioenergy) from 10 percent in 2015 to 73 percent in 2050, and the share of renewable sources in its power mix from 15 percent in 2015 to 92 percent in 2050. Deployment of renewable energy has made remarkable strides in the last decade, with growth rates far outpacing those of conventional sources, as costs have plunged in great part driven by government policies. The cost of wind turbines has fallen by nearly a third since 2009 and that of solar photovoltaic (PV) modules by 80 percent [18]. Currently onshore wind, biomass, geothermal, and hydropower are all competitive or even cheaper than fossil fuelfired power stations. Least-cost solutions for energy access needs can be more-easily determined through integrated tools, such as LEAP (Long-range Energy Alternatives Planning System) and the open source Electrification Pathways tool [31]. Innovative business models that use mobile communication technology platforms and pay-as-you-go (PAYG) financing have allowed consumers to overcome the upfront costs of decentralized energy solutions in poor communities [14]. Moreover, publicprivate partnerships have reduced risks and improved project bankability [18], such as Bangladesh’s Infrastructure Development Company Limited (IDCOL) solar home systems program, which has installed over 4 million systems by November 2016 [9], making it the world’s largest off-grid electrification scheme. However, in order to achieve the needed deployment rates of renewable technologies in these regions, as well as the needed improvements in use and conversion efficiencies, investments would have to be scaled up significantly. Even though ASIA leads global investments in renewables with an estimated USD 161 billion in 2015 [18], over half of total global investments in renewables, and MAF has been attracting increased levels of investment, having summed USD 12 billion in 2015 [18], these levels of investments fall considerably short of those needed to meet climate goals [17]. Globally, currently planned cumulative investments of USD 9.6 trillion between 2015 and 2050 in renewable energy would need to

increase over two-fold to help achieve the level of decarbonization needed to meet the 2 °C target [19]. Similarly, currently planned cumulative investments of USD 29 trillion between 2015 and 2050 in energy efficiency would need to increase almost two-fold. Nonetheless, the realization of such additional investments faces at least three critical barriers. First, the high cost of capital to invest in most part of MAF and several parts of ASIA due to the high level of risks (e.g. political, credit, currency, and offtake risks) perceived by lenders. Second, the lack of local capacity and resources for project development [18]. Third, the crowding out of public funds from other socially-sensitive sectors [19]. Innovative initiatives in public financing could help mitigate the perceived risks and lower the cost of borrowing, by de-risking investments and aligning them with sustainable energy access requirements. Such public funding could specifically focus on scaling up decentralized energy solutions in rural communities, the most under-served within the energy poor population, which has received less than 1 percent of the total trackable finance committed for electricity [31], by streamlining regulations and providing targeted subsidies [14]. To help build and strengthen local capacity and resources, adequate outreach and training programs that strengthen local organizations’ leadership capacity should be pursued through implementation partnerships between local and international NGOs, development agencies, and local communities. Lastly, policy measures and regulations could limit the amount of effective crowding out, even without compromising regional financial stability. For example, setting carbon taxes and phasing out fossil fuels subsidies could generate new sources of capital that limit the crowding out effect. Global fossil fuels subsidies were estimated at USD 384 billion in 2013, without considering environmental externalities, with ASIA accounting for 18 percent and MAF over 47 percent, approximately [3]. Non-traditional sources of financing, such as community-based finance, could also be fostered as a means to further reduce crowding out effects.

6. Conclusions Even though the projections obtained here should be appreciated in terms of their high-level estimation, they indicate that, even in the most stringent of the climate action scenarios considered, emissions associated with achieving higher levels of human well-being in all regions where improvements are still needed could exceed estimated carbon budgets associated with a reasonably high chance of limiting the temperature increase to below 2 °C by almost 30 percent by mid-century, primarily in ASIA. Notably, the impact could be even greater should: 1) MAF be able to increase its levels of well-being beyond the projected 6 percent, and 2) lower temperature increase limits be applied towards climate stabilization efforts. While lower than the projections obtained in Ribas et al. [29], the findings here continue to showcase the extent to which climate stabilization could be affected by policies and actions towards closing the existing energy divide and enabling the achievement of higher well-being. This outcome continues to somewhat contrast with the findings in the 2016 edition of UNEP’s Emissions Gap Report [47] and in the 2013 edition of the IEA’s World Energy Outlook [12]. In both reports the additional energy consumption needed to achieve SDG 7 is not expected to have significant impact on global emissions. Both reports, however, entail increased energy consumption levels associated with meeting only minimum living conditions, and not higher levels of human well-being as intended and projected in this and in the previous study.

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The assessment presented here also suggests that the sooner and/or more stringent new climate policies and actions are taken, the lower the carbon impact of the additional energy needed to achieve higher levels of human well-being. However, to close the emissions gap in ASIA, a dramatic and immediate up-scaling of current levels of deployment of clean energy and energy efficiency measures would be required. For the buildings sector in ASIA, the region with the greatest impact, we recommended a combination of effective policy instruments, including building performance codes and standards, energy-efficiency labelling and certification, and public procurement regulations. To foster the necessary uptake of clean and efficient energy solutions in residential, commercial and industrial buildings it would be advantageous to support sub-national and local control of local codes and standards. Moreover, to optimize synergies between deployment of clean energy and energy efficiency gains, new energy policies should prioritize smart-grid, district energy systems and community energy plans. In conclusion, a fundamental complementary policy direction would involve promoting behavioral changes towards more sustainable lifestyles, such as those associated with energy use in households. After all, the transition to the levels of clean technology deployment needed to meet the internationally agreed climate stabilization targets is not expected to be completed before midcentury. In fact, neither is the technological progress needed for large-scale use of carbon dioxide removal or negative emissions technologies, typically integrated into climate stabilization pathways [6]. Further research would be warranted on the critical role of adherence to climate-friendly behaviors in closing the energy divide and enabling the achievement of higher levels of human wellbeing without compromising climate stabilization efforts. Acknowledgment We thank the IAM MESSAGE modelers for producing and making available their model output and the International Institute for Applied System Analysis (IIASA) for hosting the IPCC AR5 Scenario Database. The authors would also like to thank Rao Narasimha (Yale University) and Joana Portugal (Imperial College) for their collaboration in updating the original assessment, as well as Hartmut Krugmann for his valuable feedback during the preparation of earlier versions of this paper. A.F.P.L. and R.S. would like to acknowledge the financial supported received from the National Council for Scientific and Technological Development (CNPq) of Brazil. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.enbuild.2019.06.053. References [1] K.J. Arrow, P. Dasgupta, L.H. Goulder, K.J. Mumford, K. Oleson, Sustainability and the measurement of wealth, Environ. Dev. Econ. 17 (2012) 317–353. [2] P. Chen, S. Chen, C. Chen, Energy consumption and economic growth-New evidence from meta analysis, Energy Policy 44 (2012) 245–255. [3] D. Coady, I.W.H. Parry, L. Sears, B. Shang, How Large are Global Energy Subsidies?, 2015 Washington D.C, IMF Working Paper. [5] R. Day, G. Wlaker, N. Simcock, Conceptualising energy use and energy poverty using a capabilities framework, Energy Policy 93 (2016) 255–264. [6] S Fuss, JG Canadell, GP Peters, M Tavoni, RM Andrew, P Ciais, RB Jackson, CD Jones, et al., Betting on negative emissions, Nature Clim. Change 4 (2014) 850–853. [7] J. Goldemberg, A.K.N. Reddy, K.R. Smith, R.H. Williams, Rural energy in developing countries, in: J. Goldemberg (Ed.), World Energy Assessment: Energy and the Challenge of Sustainability, United Nations Development Program, New York, 20 0 0.

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