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Abstract Kenya has made considerable policy efforts to expand its renewable energy portfolio to meet energy demand and mitigate greenhouse gas (GHG) emissions. Development of proper policies requires a robust framework for analyzing the benefits of renewable energy investments. Towards this end, this study applied a choice experiment analysis to determine how attributes (type of energy, ownership, impact on environment, distance and visibility, community job creation, and yearly renewable energy tax) impact the public willingness to pay for renewable energy development in Kenya. A nationwide survey of 1020 households was conducted in nine counties using conditional logit (MNL) and random parameter logit (RPL) frameworks. The results reveal that the Kenyan public places a high value on environmental impact, followed by type of renewable energy and community job creation, respectively. On the other hand, respondents do not place much emphasis on ownership or distance and visibility. Policy simulation suggests that while renewable energy adoption is highly valued by households, the total willingness to pay is not enough to cover the higher capital cost for the development of various renewable energy technologies.

Abstract Eradicating poverty and mitigating greenhouse gas (GHG) emissions are core issues of global sustainable development goals (SDGs), and China is struggling in realizing these targets. The poverty reduction that leads to popualtion structure and lifestyle changes would have an impact on GHG emission changes. However, few studies have assessed the historical and future impacts of the poverty allevation on China's emissions. Here by linking Chinese Multi-Regional Input Output (MRIO) database to the global MRIO database EXIOBASE, and using provincial household consumption data, we identified the distribution of Chinese household greenhouse gas footprints (HGFs) by income groups in 2015 at the national and provinical levels. Moreover, we focused on the historical impact of poverty alleviation on HGFs during 2010–2015, and developed four scenarios to project future HGFs changes due to poverty alleviation by 2030. We find that eradicating extreme poverty in the secanrio S2, i.e., bringing people to an income above $1.9 daily, does not cause a large emission impact with current technological level. However, lifting people from a higher poverty line of $5.5 per day in the sceanrio S4 results in a 1.6% increase in emissions compared with the scenario S1 without any poverty reduction goals. Furthermore, realizing a higher poverty reduction target will result in an increase of emissions contribution from internatioanl supply chains due to the differences in consumption patterns among different income groups. Our study highlights the conflict between the high poverty alleviaition goal and emission reduciton in China, and reminds us of the need to make more technological efforts for avoiding the large emissions embodied in international supply chains.

Abstract This paper provides the first estimates of China's global developmental finance institutions in general and China's policy bank lending to foreign governments for energy in particular. According to the China Global Energy Finance database, between 2000 and 2017, China Development Bank (CDB) and China Export-Import Bank (CHEXIM) provided $225.75 billion in overseas energy development finance. We find that: China's ‘policy banks’ and funds have doubled the availability of global development finance –and hold more assets than the major Western-backed MDBs operating in developing countries. With the onset of a new family of funds and multilateral development banks co-financed by China, China is poised to be the largest development lender in the world as Western-backed MDBs appear stagnated in their ability to increase their capital bases. China's global energy portfolio is heavily exposed to country, macroeconomic, climate, and social risks, however. To mitigate such risks and meet the broader sustainable development challenge for the 21st Century, China's development finance will need to shift the composition of its global energy lending in a significant manner.

Abstract The ecological footprint is a measure of the resources necessary to produce the goods that an individual or population consumes. It is also used as a measure of sustainability, though evidence suggests that it falls short. The assumptions behind footprint calculations have been extensively criticized; I present here further evidence that it fails to satisfy simple economic principles because the basic assumptions are contradicted by both theory and historical data. Specifically, I argue that the footprint arbitrarily assumes both zero greenhouse gas emissions, which may not be ex ante optimal, and national boundaries, which makes extrapolating from the average ecological footprint problematic. The footprint also cannot take into account intensive production, and so comparisons to biocapacity are erroneous. Using only the assumptions of the footprint then, one could argue that the Earth can sustain greatly increased production, though there are important limitations that the footprint cannot address, such as land degradation. Finally, the lack of correlation between land degradation and the ecological footprint obscures the effects of a larger sustainability problem. Better measures of sustainability would address these issues directly.

Abstract Energy is indispensible in modern society and is one of the most important components of socio-economic development. Nepal is one of the least developed countries with more than 80% of its population residing in rural communities. Per capita energy usage – often viewed as a key index of the development – in the country is far less than the global average per capita energy usage. The energy sector is dominated by the traditional energy sources such as fuel woods, crop residues and animal dung mainly for domestic usage contributing to about 86% of the national energy consumption. Currently 40% of the population has access to electricity, and the rural electrification accounts for only 29%. The majority of rural populations are meeting their energy needs by burning biomass in traditional stoves which has several environmental and public health issues. Nearly all fossil-derived fuels consumed in the country are imported in a refined form, and the perpetual increase in petroleum imports has adversely impacted the existing fragile economy of the country. Despite a huge potential in harnessing various renewable energy resources such as hydropower, solar power, wind energy and biofuels/bioenergy, these resources have not been sustainably captured due to geographical, technical, political and economical reasons. This paper presents a brief account of Nepal's renewable energy resources and the current status of various renewable energy technologies (RETs) such as micro-hydro, solar power, wind energy, biofuel/bioenergy, improved cook stoves, and improved water mill. It also highlights the opportunities and barriers for the development of RETs. Finally this paper presents some recommendations for the promotion, development and implementation of RETs in the country.

Abstract Industrial sustainability has gained huge attention in the scientific community owing to the concern of global warming. Establishing sustainability indicators by the application of exergy analysis can guide and motivate industrial sustainability. In this context, an attempt is made to establish sustainability indicators for the industrial sector. Different measures to improve the sustainability of the industrial sector of Bangladesh are also discussed. Based on the energy consumption data from the year 2000–2015, energy, exergy, and sustainability analyses are performed. It is found that the energy efficiency varies from 55.01% to 59.67% and exergy efficiency varies from 53.11% to 56.97%. Exergy efficiency is found to be at lower side due to the accounting of irreversibility. Various sustainability indicators such as sustainability index, depletion number, exergetic renewable share, cumulative exergy loss, and non-renewable exergetic share have been studied as well. It is observed that depletion numbers vary from 43% to 45% and the sustainability index varies from 2.21 to 2.32. Non-renewable exergetic share is as high as 98% and maximum cumulative exergy loss is found to be 217.3 PJ in 2015. Improvement potential shows a continuous increase from 22.75% in 2000 to 101.89% in 2015. Waste heat recovery, energy audit, waste minimization, and adopting renewable energy sources are recommended to increase the efficiency and sustainability of this sector. The outcome of this study reveals that exergy analysis is an effective technique to develop energy conservation policies for this sector.

Abstract The rapid urbanization in the Belt and Road Initiative (BRI) area has aggravated the cross-regional pollution of PM2.5 and aroused concern about the conflicts between urban development and air quality. This study aims to examine the spatial-temporal PM2.5 variations in the BRI region, in which area many countries are undergoing rapid urbanization and the main field of future urbanization, to delineate the driving mechanism of PM2.5 accumulation or dissipation. Previous studies have analyzed the PM2.5 distribution at the national level, providing limited information regarding regional heterogeneity in urbanization and PM2.5 concentrations within each country. Additionally, the regional differences in the driving mechanisms of urbanization factors on PM2.5 concentrations have not been thoroughly investigated within the BRI areas. In this study, remote sensing raster data was combined with geographic grid units to examine variations in urbanization and PM2.5 within the BRI region, identifying “typical regions” where urbanization could enhance PM2.5 accumulation. The main results are as follows: i) The spatial autocorrelation of urbanization and PM2.5 concentrations has gradually strengthened, showing consistent high-value distributions in the North China Plain, Ganges Plain and indicating a synergistic growth among emerging developing regions such as China, India, and the Persian Gulf Coast. ii) The correlation between urbanization and PM2.5 concentrations exhibited a distinct trend of differentiation within the BRI regions. The influence of urbanization on PM2.5 changed from agglomeration to dispersion, forming a “typical region” category composed of ten countries, including China, India, and Morocco. iii) The three main urbanization-related factors for PM2.5 accumulation in the “typical regions” for 2005–2016 were energy pollution emission, economic activities, and human activities. By 2023, the effects of energy pollution and economic activities are expected to converge in some “typical region” countries. Targeted urban strategies and governance actions based on the different driving-types of “typical regions” in BRI have been proposed to coordinate relationship between urban construction and atmospheric environmental protection.

Abstract Artificial Intelligence (AI) is becoming the engine of a new round of technological revolution and industrial transformation; as such, it has attracted much attention of scholars in recent years. Surprisingly, scarce studies have shed lights on the effects of AI on the environment, especially with respect to carbon intensity. Based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, we use Chinese industrial sector data from 2005 to 2016 to investigate how AI affects carbon intensity. The empirical results show that AI, as measured separately by the adoption of robotics by industry and the number of academic AI-related papers, significantly reduces carbon intensity. The results remain robust after addressing endogenous issues. We find that there are both stages and industrial heterogeneity in the effects of AI on carbon intensity. AI had a more decrease effect on carbon intensity during the 12th Five-Year Plan than the 11th. Compared with capital-intensive industries, AI tends to have a more decrease effect on carbon intensity in the labor-intensive and tech-intensive industries. To enlarge the effects of AI on reducing carbon intensity, the government should promote the development and application of AI and implement differentiated policies in line with the industry characteristics.

Abstract This paper examines the relationship between hydroelectricity consumption and economic growth in China, while controlling for fossil fuel consumption, financial development, capital, institutional quality and globalization and its components for the period, 1970–2014. We have employed the Bayer and Hanck, (2013) combined cointegration test to examine the long-run relationships between those variables as well as the autoregressive distributed lag method with structural breaks as a robustness check. The empirical findings demonstrate a long-run relationship between those variables. Hydroelectricity consumption, fossil fuel consumption, capital, financial development and globalization and its components have a positive influence on GDP in China. The findings also provide predominant evidence on the long-run feedback hypothesis between the variables. The findings suggest that policies should be implemented to increase the role hydropower in the energy mix for sustainable economic growth in the country.