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It is highly important to clarify the determinants of China’s energy consumption for the smooth implementation of energy conservation. For this purpose, this paper discusses the contribution of China’s different economic sectors to decoupling progress. Then, it uses an extended logarithmic mean Divisia index (LMDI) method to decompose China’s energy consumption into seven factors. The major findings indicate that during the 2000–2016 period, (1) the decoupling status between China’s economy and energy consumption were determined largely by the secondary industry; (2) the positive contribution of three sectors to the decoupling process was ranked as follows: primary industry>tertiary industry>secondary industry; (3) China’s energy consumption showed a growing trend, and the expansion of per capita fixed asset investment was the main factor responsible for the energy consumption increase, especially in some provinces whose investment structure was skewed towards energy-intensive industries. Additionally, population growth had mild positive impacts on the energy consumption increase; (4) declines in energy intensity and investment efficiency were two major contributors to energy-saving. However, economic structure, energy structure, and spatial structure adjustment failed to reduce energy consumption but had overall positive impacts on the energy consumption increase. Finally, several policy recommendations related to reducing energy consumption are proposed.

Particularly for developing regions like Sub Saharan Africa, energy intensity is a critical policy issue and usually affected by four challenges: substitution between energy and other factors, technological change, changes in energy composition, and changes of economic growth. This paper examines energy intensity, output elasticity, energy-capital-labor substitution possibility, and factors contribution rates. The study applies a Translog production approach with data spanning 1990–2014 and further, applies ridge regression technique as a robust rectification to achieve unbiasedness in the findings. There is expected evidence of inverse relationship: higher energy intensity in lower per capita economy, evidenced by upward energy tariffs with lower energy elasticity. Factors are inelastic and have average elasticity substitution possibility for capital-energy with their inferential rebound effects’ challenges. However, other combinations indicate complementarity at the current development stage. We further, observe that if higher energy intensity not managed, would translate into higher CO2 intensity, because 99% of total energy is sourced from hydrocarbons of 51% and 48% of biomass. Based on the findings, policymakers and implementors are encouraged to integrate and optimize the whole energy system towards improving energy efficiency through increasing renewable energy composition from the current 1% to at least 10% whiles increasing quality-energy from nonrenewable.

In recent years, the rapid development of China’s economy has significantly stimulated demand for electricity consumption. After China’s economy entered the phase of “New normal” with moderately lower growth rates, the growth of electricity consumption has also slowed down, and there seemed to be a trend of gradual decoupling between these two variables. To investigate whether and how electricity consumption changed along with economic growth, 100 prefecture-level cities’ dynamic panel data are used in this study to examine the relationship between the decoupling indicator of electricity consumption and economic growth. To control for the potential endogeneity, the first-order difference Generalized Method of Movements (GMM) estimator is employed. The empirical results indicate that the relationships between the decoupling indicator of electricity consumption and real GDP per capita differs across different regions. For the whole nation and the eastern region, there is a significant “inverted N-shaped” relationship between the two factors, and decoupling indicators are still rising along with economic development. In contrast, in the central and western regions, there is a significant “N-shaped” relationship between the two factors. Besides, other macroeconomic factors, including industrial structure, FDI and financial investment in technology, may also affect the decoupling indicator of electricity consumption.

The rapid global economic development over the past two decades has been accompanied by rising energy demand and CO2 emission rates. Understanding the driving forces of CO2 emissions is necessary for future energy planning and policy formulation. This paper examines the driving factors behind the increase in CO2 emissions in Malaysia with special focus on the manufacturing, electricity and transportation sectors. The paper extends the Kaya identity by incorporating energy mix, investment efficiency, capital-labor substitution, population-to-employment, urbanization rates, and per capita CO2, along with the standard variables including economic activity, economic structure, and energy intensity. The paper also evaluates if any macroeconomic instability (economic crisis) in the country has led to reduction in emission rates. The study utilizes the latest country data, covering the period 2002–2016. The result shows that, while economic activity is always associated with CO2 emissions, more efficient energy use would help restrain the rise in emission rates without hampering economic growth regardless of economic structure. Emission reduction targets in Malaysia should be reinforced with an appropriate economic restructuring that restrains the economic structure effect. Due to the extensive energy use in the transportation and electricity generation, future emission control should focus more on these sectors.

Asia has become a major emitter of carbon emissions. To better achieve emission reduction targets, it is necessary to study the characteristics of carbon dioxide emissions in Asia and their emission reduction potential. Firstly, the gravity movement of Asia's carbon dioxide emissions and its leading forces are explored. Then, the Logarithmic Mean Divisia Index method is applied to find the driving factors governing carbon dioxide emissions. Furthermore, this paper establishes a theoretical model of carbon dioxide emission reduction. At last, the influencing factors are utilized to construct a decoupling index, which is used to describe the dependence of economic development on carbon dioxide emissions. The gravity center for Asia's carbon dioxide emissions was an overall movement towards southwest. However, the gravity center for carbon dioxide emissions per capita was an overall movement towards the southeast. The energy intensity effect was the most factor decreasing Asia's carbon dioxide emissions. Only five countries achieved theoretical reduction of carbon dioxide emissions over 2000–2015. However, three countries were failed to achieve the theoretical reduction of carbon dioxide emissions in this period. The economic development in most east Asia and southeast Asia countries was less dependent on energy-related carbon dioxide emissions over the period 2000–2015.

The study analyzes the effects of carbon dioxide emissions, energy use, gross fixed capital formation, real GDP per capita, exports and imports on Indian economic growth. The study seeks an answer to the following question: When examining the relationships that exist between these parameters, what are the driving forces behind the growth trajectory of the Indian economy? An ARDL model was applied to the analysis of annual data so as to determine co-integration on variables and the nature of the relationship for the period 1971–2014. Empirically, it was found that in the short-term, Real Gross Domestic Product per Capita (LGDPP) for the Indian economy is affected by its past value, Gross Fixed Capital Formation (LGCAPP), Energy Consumption (LENERP) Carbon Emissions (LCO2P) and Imports (LIMPP); however, in the long-term, Gross Fixed Capital Formation (LGCAPP) and Exports (LEXPOP) played a significant role. The error correction model revealed the existence of some co-integration relationship for the entirety of the variables in the study for the short-term and long-term. It is concluded that policies should have clear alternative renewable energy consumption targets in order to achieve energy efficiency and achieve sustainable growth of the economy, while reducing carbon dioxide emissions.

The timely and effective evaluation of spatiotemporal patterns of urban electricity consumption is a critical prerequisite for establishing policy on sustainable electricity utilisation in China. However, calculating urban electricity consumption in China is difficult due to the confusion generated by the various city definitions and corresponding urban boundaries. Using Chongqing as a case study, this study was the first attempt to evaluate and compare the spatiotemporal patterns of urban electricity consumption within different spatial boundaries. Four urban boundaries, including the city administrative area, city district, urban centre, and urban built-up area, were defined using the administrative boundaries and urban built-up area data. Then, the electricity consumption was estimated at a 1-km spatial resolution from 1992 to 2013 using the nighttime light data and statistical electricity consumption data. Finally, the temporal and spatial evolution of urban electricity consumption within different boundaries were evaluated and compared from multiple perspectives. The results showed that a rapid increase in urban electricity consumption occurred in the four urban boundaries in Chongqing from 1992 to 2013. The urban electricity consumption in urban built-up area accounted for 34.34%–45.69% of that in city administrative area from 1992 to 2013, which indicated that urban built-up area was still the centre of electricity consumption in Chongqing. There was a very low-gridded urban electricity consumption with significant spatial variability in city administrative area, city district, and urban centre; however, there was a wide distribution from 10.03 to 20.21 million kWh in urban built-up area. Special attention should be given to urban built-up area, which presented the highest per capita urban electricity consumption among the four urban boundaries, with values from 18,470 kWh in 2005 to 20,370 kWh in 2010. Our results also noted that the urbanisation rate has become the strongest driver of urban electricity consumption within the different urban boundaries in Chongqing, with R2 values larger than 0.95. This study suggested that decision makers should explicitly state the accounting boundaries to avoid data gaming and inaccurate results when designing benchmarks or plans or when analysing the performance of urban electricity consumption.

In the present “new normal” economic mode, the household is a major driver of China's electricity consumption growth. To guide the development of the electric power industry in adapting to this situation, this study used the household electricity consumption and population data of 30 provinces during 2001–2014, a three-dimensional decomposition model, and a hybrid trend extrapolation model to explore the driving factors of China's household electricity consumption growth and forecast its future development trend before 2030. Empirical analysis drew the following conclusions: (1) China's household electricity consumption growth is mainly attributed to the improvement of its living standards and still has great potential. (2) Population increase and provincial population structure adjustment have little impact on household electricity consumption growth. (3) In 2030, China's household electricity consumption per capita will increase to 1.06 thousand kWh per capita. (4) China's household electricity consumption will increase to 1.57 trillion kWh in 2030, which is twice that in 2015. The implementation of the universal two-child population policy will have no significant impact on these forecasting results. (5) Raising household electric price level, setting cool and heat storage price, and developing the micro-grid are the suggested policy directions.

The implementation of Increasing Block Electricity Prices (IBEPs) aims to guide residents towards electricity savings and rational energy use, but there are questions over the effectiveness of the IBEPs to achieve these goals. To this end, this paper uses residential IBEPs policy implemented in Sichuan Province of China in 2006 as a natural experiment to answer the question of whether IBEPs effectively regulate residents’ electricity demand. Synthetic control method (SCM) was used to evaluate the treatment effect of the policy. The paper finds that the IBEPs policy significantly reduces urban and rural residential electricity consumption. The per capita electricity consumption of urban residents fell by 26.87–100.76 kWh/year with an average of 51.40 kWh/year, equivalent to a decrease of 5.93%–17.50% and average of 11.17%. On the other hand, the per capita electricity consumption of rural residents decreased by 20.86–48.28 kWh/year with an average of 26.28 kWh/year, which is equivalent of a decreased of 7.8%–16.79% and average of 12.75%. Electricity demand in urban areas decreased more than in rural areas, but rural residents are more sensitive to IBEPs than urban residents. In order to achieve “equity” and “efficiency”, China needs to further improve the design mechanism of residential IBEPs.

Figuring out the convergence and persistence of per capita CO2 emissions matters much for environmental policy makers in both developed and developing countries. The Kyoto Protocol aims at avoiding threat from climate warming for human beings. CO2 emissions have been viewed as the main cause of climate change in recent decades. Thus, loads of empirical studies contribute to investigate the convergence of per capita CO2 emissions by implementing various econometric models including as many sample countries as possible. By applying a battery of univariate unit root tests, quantile unit root test, and a newly developed quantile unit root test with Fourier function, we re-investigate the convergence, mean-reverting properties and asymmetric behavior of per capita CO2 emissions in 21 OECD countries. The findings show that per capita CO2 emissions of Austria, Finland, Japan, Netherlands, New Zealand, Norway, Sweden, Switzerland and the US perform converging as a whole from the perspective of the FQKS statistics. Besides, mean-reverting properties are identified for Austria, Finland, Japan, Netherlands, Norway, Sweden, Switzerland and the US when economy is in recession. Finally, asymmetric behaviors of per capita CO2 emissions are detected at selected quantiles. All of the results provide impressive environmental economic implications for policy makers.

Natural gas consumption gradually rises in all sectors for being a relatively economical and clean fuel compared to other substitutes. In a step towards adopting environmentally friendly fuel and to save foreign exchange, as the domestic gas reserves could be utilized. Under policies, Pakistan's natural gas consumption is expected to commensurately rise, which will significantly impact environment, energy security and the economy. This study applies the Logarithmic Mean Divisia Index and sectorial Intensity Decomposition Methods to estimate natural gas consumption factors. The objective is to analyze some relevant novel factors (i.e., fossil energy structure, non-renewable energy structure, energy intensity, GDP per capita, and population) that might give a new-way for policy-makers. Results show: (1) GDP per capita and population are most imperative driving factors in rising natural gas consumption. The energy factors are the most significant factors followed by per capita GDP, population, energy intensity, and industries. (2) Energy intensity in transport sector contributed highly to energy intensity. (3) The scenarios predicted that the average yearly growth rate of gas consumption is predicted to approximately 24.37% from 2020 to 2030. Prediction highlights that sustainability and energy security could be ensured by regulating environmental policies and technical progress. Further targeted policies are provided.

China is the world's biggest energy consumer and carbon emitter; this is a great challenge to both global and national environmental security. To achieve energy conservation and emission reduction goals, it is essential to identify the driving factors that affect the energy interaction patterns of the Chinese economy. For this purpose, the spatial independent variable lag model (SLX) and structural decomposition analysis (SDA) methods were integrated to detect province-level consumption-based energy changes induced by five driving factors, and due consideration was also given to interregional spillover effects from both static and dynamic perspectives. The results of this study uncovered that energy intensity acted as a major driver for energy conservation both in the short- and long-terms, whilst the role of energy intensity in curbing energy consumption was weakened. In contrast, optimizing production structure offers the potential for big energy reductions and it was further noted that spillover effects contributed to both national and regional energy conservation. Consumption per capita and population size presented similar static effects on energy increases. The spillover effects of the former had a greater impact on energy consumption growth during the investigated period, whilst both static and dynamic processes of consumption structure failed to form fixed direct and spillover effects on energy use at the regional level. The findings of this study contribute to understanding the regional heterogeneity of driving forces for energy consumption and provide a solid foundation for making effective energy conservation regulations.

China has become the world's largest carbon dioxide emitter and energy consumer, with serious environmental problems. Therefore, low-carbon economy has become the best choice for its development. In this paper, the economy, society, energy and environment are taken into account to build a low-carbon economy system (LCE). It is used to analyze the drivers of CO2 emissions and impact of low-carbon transition in China. Bayesian network and scenario analysis are applied. The results show that the increase of per capita consumption expenditure, energy consumption per unit of GDP, energy consumption per capita and CO2 emissions per capita is the reason for the continuous growth of CO2 emissions; the improve of energy efficiency, CO2 emissions per unit of GDP and CO2 emissions per unit of energy consumption inhibits CO2 emissions. China's low-carbon development does not come at the expense of its economic growth. Actually, the low-carbon transition is accompanied by the growth of population, consumer spending, energy consumption and energy efficiency. Urbanization rate will be up to 75.70%–97.44%. Compared with 2017, energy efficiency increases by 4.51%; energy consumption per unit of GDP reduces by 0.4%. Transformation of energy subsystem is the key to a low-carbon road.

The link between climate and energy consumption has been widely established in many cities. The residential electricity consumption majorly for space conditioning is especially responsive to the outdoor climate. As a hot and humid tropical city, Singapore is expected to see increasing residential electricity consumption with global climate change. In this study, a multiple regression analysis is performed to relate the residential electricity consumption per capita and per household with outdoor climate during a 12-year period (2005–2016). The per-capita electricity consumption during 2008–2016 sees substantial decrease from that during 2005–2007, possibly due to the various energy saving measures taken by Singapore government. The different responses to outdoor climate variation in different household types are examined. It is found that generally dwellers in public households are more sensitive to outdoor climate variations, while private household occupants are not much affected by climate. Due to increasing ownership of electricity-consuming appliances in 1–2 Room and 3 Room public households, their electricity consumptions show a consistent increasing trend during the period studied. Under the worst scenario of projected future temperature increase, the per-capita and per-household electricity consumptions are projected to increase by 47% and 41%, respectively.

Energy security assessment provides a benchmark for policy analysis and identifies the challenges for ensuring energy supplies as well. This paper develops a composite index for assessing the oil supply risk of South Asian countries. The index is based on a comprehensive set of indicators including the ratio of imported oil over GDP, geopolitical risk, market liquidity, GDP per capita, ratio of oil imports over consumption, diversification, oil price volatility, US$ volatility and transportation risk. Results reveal that India is the least oil vulnerable country while Afghanistan and Bangladesh are the most oil vulnerable countries. India's leading score reflects a higher potential to change the oil suppliers while Afghanistan, Bangladesh, Bhutan, and Nepal have the least score confirming them as the highest supply risk associated countries. Policies such as adopting renewable energy technologies, generating nuclear power, diversifying export sources and cutting down oil subsidies can help reduce the impact of oil supply risk.

Primary energy plays a critical role in the socio-economic development of China, and accurate energy consumption forecasting can help the government to formulate energy policies. To do this, the present study aims to apply a self-adaptive multi-verse optimizer (AMVO) to optimize the parameters of the support vector machine (SVM). It employs a rolling cross-validation scheme to predict China's primary energy consumption in which the independent variables are gross domestic product (GDP) per capita, population, the urbanization rate, the share of the industry in GDP and coal's share of primary energy consumption. The results indicate that the hybrid AMVO-SVM model has higher precision than other models. Finally, we apply the hybrid AMVO-SVM model to predict the energy consumption of China between 2017 and 2030 in five scenarios. In the reference scenario, China's primary energy consumption will reach 4839.3 Mtce in 2020 and 5656.2 Mtce in 2030.

Recently the expansion of urban-rural income inequality and the rapid growth of fossil-energy consumption have become two significant challenges China should address to achieve its ambitious goals. For the first time, this study uses the Chinese provincial panel data during the period of 1996–2013 to perform a quantitative analysis on the relationship between the urban-rural income gap and the per capita electricity consumption. Fully considering the potential endogeneity problem, this paper uses the orthogonal-difference Generalized Method of Moments (GMM) as the benchmark estimation method. To ensure the robustness of the estimations, the Theil index and per capita urban-rural income are utilized to measure the urban-rural income gap. The estimation results indicate that the influences of urban-rural income inequality on electricity depend on the income level. At the current stage of economic development, the income disparity has significantly negative impacts on provincial per capita electricity consumption. Moreover, there is also strong evidence for the existence of the inverted U-shaped relationship between per capita electricity consumption and GDP per capita. Furthermore, other economic and social factors, including the enhancement of urbanization and industrialization levels, the adjustment of the population structure, and the development of import-export trade may also promote electricity consumption.

In the current context, climate change has become an unequivocal phenomenon. Although it primarily encompasses change in temperature, nevertheless other weather variables such as rainfall, wind speed, evaporation and humidity can also be affected as a result of climate change. Addressing the impacts of climate change on electricity demand is essential for predicting the future demand. For example, cooling and heating requirements change significantly with respect to climate change that may result to the change in electricity load demand. In this paper, a backward elimination based multiple regression approach is proposed for analyzing the influence of climatic variables on load forecasting. A correlation analysis has been carried out using Pearson’s correlation coefficient to examine the interdependency between different climatic variables in the context of Sydney, one of the most densely populated cities in Australia. Regression based analysis has been performed to examine the relationship between per capita electricity demand and associated climatic variables. ‘Degree Days’ concept has been utilized to determine balance point temperature. Backward elimination based multiple regression is used to exclude non-significant climatic variables and evaluate the sensitivity of significant variables related to the load demand. Average change in future per capita electricity demand has been predicted using the proposed approach for the city of Sydney, Australia. Results indicate that the demand for Sydney will increase by 6% by 2030.

China is facing unprecedented pressure to control its carbon emissions as it is the biggest emitter across the world. The Chinese central government has shown its ambition to reduce carbon emissions by proposing to change the current energy structure by encouraging the use of renewable energy. To identify how this development can help China improve its emissions reductions, we measure the influence of renewable energy generation in China on its carbon emissions through a quantile regression model and path analysis of inter-provincial panel data from 2008 to 2017. The results indicate that, first, renewable energy development has an inhibitory influence on carbon emission intensity; for every 1% increase in renewable energy development, carbon emission intensity decreases in the range of 0.028%–0.043%. Second, based on the results of the quantile regression, the development of renewable energy at different levels has an inhibitory effect on carbon emissions. Third, the development of renewable energy has a less direct effect on carbon emissions, although its indirect effect is relatively large; specifically, two indicators, energy intensity and GDP per capita, have an inhibitory influence on carbon emissions.

Infrastructure development and energy efficiency improvement are crucial to the high-quality transformation of the economy. However, little literature discusses how infrastructure development affects the energy efficiency gap between regional manufacturing. This paper estimates the per capita capital stock of economic infrastructure and the green total factor energy efficiency of China's manufacturing industry. Unlike the prior grouping according to geographical location or other standards, this paper conducts a data-driven clustering method and convergence analysis on energy efficiency. Thirty provinces eventually converge into four clubs, and the gaps in energy efficiency between various clubs are found to persist. The results of the panel-ordered Probit model indicate that infrastructure development can reduce regional discrepancies in manufacturing's green total factor energy efficiency. This effect is heterogeneous in transportation infrastructure and power infrastructure. As time goes on, the marginal effect of infrastructure diminishes. Moreover, the established infrastructure reduces the regional disparities in energy efficiency, while the infrastructure under construction instead widens the gap. Some relevant policy recommendations are provided for future infrastructure planning and green development strategy based on the conclusions.

This study used the Autoregressive Distributed Lag (ARDL), The Error Correction Model (ECM), and ECM-Granger Causality to investigate the short and long-run environmental impact of renewable power and the transportation sectors in Iran over 1971 to 2015. The robustness of the results was checked by estimating three cointegration regression models. The empirical finding of this study confirmed the long-run cointegration among the variables. Indeed, the positive and significant impact of real GDP per capita, the number of vehicles, and urbanization on the CO2 emissions per capita were supported. Furthermore, the potential for renewable electricity in improving the environmental quality was proved in the long-run. Residual diagnostic tests and sensitivity analysis supported the appropriateness of the model and the robustness of the results from the ARDL. The Granger Causality supported: (1) one-way causality flowing from the share of renewable electricity to the CO2 emissions per capita (2) the complementary linkage between the number of vehicles and CO2 emissions per capita; (3) the bidirectional causality between the share of renewable electricity and the real GDP per capita. Therefore, to achieve sustainable development goals, the government and policymakers need to: increase and promote the share of renewable electricity and shift away from Internal Combustible Engine Vehicles to Alternative Fuel Vehicle.

This paper explores the crucial factors driving the changes in household energy consumption (HEC) in China during 2005–2017. We propose a decomposition framework based on the Kaya identity and the logarithmic mean Divisia index (LMDI) method to decompose the change in HEC into energy structure effect, energy intensity effect, regional structure effect, per capita consumption effect and population scale effect. We use the model to shed light on the differences of these five factors affecting HEC between urban and rural and among regions while retaining their energy-use characteristics respectively. The results suggest that: (1) Energy intensity and regional structure reduced HEC, whereas population scales, per capita consumption, and energy structure stimulated HEC growth. (2) Rural energy structure contributed larger shares of the increment in HEC. Rural per capita consumption increased generally much more energy consumption than urban counterpart in coastal developed economic regions. Rural population scale curbed the growth of HEC, while urban population scale drove the growth of HEC. (3) Although energy intensity decreased energy consumption at regional level, the differences were found between regions. Moreover, the impacts of regional structure differed significantly between regions, but insignificantly at provincial level. Finally, some policy recommendations will be made based on these suggestive conclusions.

High levels of variable solar and wind must be balanced with alternative generation, storage, transmission and demand management to ensure continuous availability of electricity. The cost of balancing has been disputed, which militates against mass deployment of solar and wind to mitigate climate change. Australia is installing solar and wind 10 times faster than the global per capita average which constitutes a natural experiment in the real cost of balancing. The National Electricity Market (NEM) and the state of South Australia have reached combined solar and wind energy penetrations of 24% and 70% respectively and are tracking towards 40% and 100% respectively in 2024–25. Investment in new balancing infrastructure is predominantly in pumped hydro (without new dams on rivers), batteries and transmission. The current (2021) and market futures (2024) spot price for electricity in the NEM and South Australia is about AUD$40 (∼US$30) per Megawatt-hour. This suggests that the balancing cost of high levels of solar and wind is modest.

This study is the first attempt to investigate the nexus among per capita carbon dioxide (CO2) emissions, gross domestic product (GDP), and natural gas and renewable energy consumption within the framework of the environmental Kuznets curve (EKC), in a 1985–2016 sample of BRICS countries (i.e., Brazil, Russia, India, China, and South Africa). For this purpose, panel unit root, cointegration, and causality tests allowing for cross-sectional dependence are conducted. Employing the augmented mean group (AMG) estimator, the results provide strong evidence in favor of the EKC hypothesis for the BRICS countries by proposing that the EKC holds in all five BRICS countries; the turning points (TPs) lie between $4282.90 (India) and $16,553.77 (China), while the turning years (TYs) are estimated to be between 2018 (Russia) and 2035 (India). Furthermore, increasing natural gas and renewable energy consumption lowers CO2 emissions, which indicates that, 1% increase in natural gas and renewable energy consumption for the BRICS countries will decrease CO2 emissions by 0.1641% and 0.2601%, respectively. The causality analysis underscores feedback hypothetical links among CO2 emissions, natural gas consumption, and renewable energy consumption in the long run. Some policy implications are highlighted for BRICS countries' policymakers not only for tackling CO2 emissions, but also for promoting growth in the natural gas and renewable energy industries.

The prime focus of this study is to explore the influence of fossil fuel consumption, electrical energy consumption, oil-based imports, and expansion of urbanization on the environmental degradation experienced in developing economies of Latin America and the Caribbean (LAC). This study has applied a system Generalized Method of Moment with a two-step estimator involving a panel of twenty middle and lower-middle income economies for the period 1990 to 2015. The results indicate that fossil fuel consumption, oil imports and urbanization expansion are significantly contributing to environmental degradation. The outcomes have also confirmed the reversed U-shaped relationship between carbon emissions and per capita growth. The findings determine that the use of renewable energy sources can help to meet rising energy demand and also lower the trade deficit by decreasing the import of oil in developing countries of Latin America and the Caribbean.

A hierarchical Bayesian model is developed for predicting monthly residential per capita electricity consumption at the state level across the USA. The summer period is selected to target cooling requirements that are generally directly associated with electricity use compared with winter heating requirements that are derived from a mix of energy sources that has changed over time. Historical monthly electricity consumption data from 1990 to 2013 are used to build a predictive model with a set of corresponding climate and non-climate covariates. A clustering analysis was performed first to identify groups of states that had similar temporal patterns for the cooling-degree-days of each state. Then, a partial pooling model is applied to each cluster to assess the sensitivity of monthly per capita residential electricity demand to each predictor (these are cooling-degree-days, gross domestic product (GDP) per capita, per capita electricity demand from the previous month and previous year, and the residential electricity price). Most of the predictors are significant for most of the states across USA. The sensitivity of residential electricity demand to cooling-degree-days has an identifiable geographic distribution with a stronger relationship in northeastern United States. This predictive model considers interactions between causal factors at large spatial scales and quantifies the model uncertainty. This study can be applied as guide in the future energy management.

Limited publications were designed to examine the Water-Energy-Carbon (WEC) efficiency of different regions. This study is the first to seek to identify the regional embodied WEC efficiency of China, including 31 of 34 provinces, using the Multiregional Input-Output approach. The provincial embodied WEC coefficients, total amount of embodied WEC, embodied amount of WEC per capita are explored. Results show that: i) Xinjiang (33 Gt/k€), Hebei (34 MJ/€) and Ningxia (5 t/k€) separately dominate the lists of provincial embodied water consumption coefficients, energy consumption coefficients and CO2 emission coefficients; ii) Shandong and Jiangsu consume the most embodied water at 109 Gm3 and 105 Gm3, embodied energy at 21 EJ and 20 EJ, and emit the most CO2 emissions at 2.7 Gt and 2.2 Gt. However, as the more developed province and with higher GDP, Guangdong has lower embodied WEC amount and higher WEC efficiency; ⅲ) The regions that have larger values of embodied WEC per capita, mainly located at the east and north-west of China, including Beijing, Tianjin, Shandong, Jiangsu, Zhejiang, Inner Mongolia and Xinjiang. This study targets the contribution to understanding the regional WEC efficiency performance, highlighting the spots and economies that deserve the uppermost attention for emissions reduction.

About 70% of Sub Saharan Africa’s (SSA) electricity generation comes from fossil fuels. However, the overdependence on these resources has contributed to the increasing global temperature levels leading to an annual average of 10%–13% decline in GDP per capita growth in the region. Besides, SSA’s contribution to Climate Change is marginal yet recognized as the hardest hit in the world. Therefore, considering technology heterogeneity, the study applies Metafrontier non-radial Malmquist index of CO2 emission (MNMCPI) to estimate the changes in the performance of CO2 emission and its decompositions in public thermal power plants across 18 SSA countries over 2005–2016 year period. The results depict that, averagely, the CO2 emission performance changes progressed slightly by 0.3% over the sample period, largely due to technological progress. Regionally, West Africa has the highest MNMCPI. Southern Africa emerged as the technology leader among all regions. The technical efficiency change index decline in all regions. Targeted policy implications are provided to enhance the transfer of advanced technologies and management experience to minimize CO2 emissions.

Australia has one of the highest per capita consumption of energy and emissions of greenhouse gases in the world. It is also the global leader in rapid per capita annual deployment of new solar and wind energy, which is causing the country's emissions to decline. Australia is located at low-moderate latitudes along with three quarters of the global population. These factors make the Australian experience globally significant. In this study, we model a fully decarbonised electricity system together with complete electrification of heating, transport and industry in Australia leading to an 80% reduction in greenhouse gas emissions. An energy supply-demand balance is simulated based on long-term (10 years), high-resolution (half-hourly) meteorological and energy demand data. A significant feature of this model is that short-term off-river energy storage and distributed energy storage are utilised to support the large-scale integration of variable solar and wind energy. The results show that high levels of energy reliability and affordability can be effectively achieved through a synergy of flexible energy sources; interconnection of electricity grids over large areas; response from demand-side participation; and mass energy storage. This strategy represents a rapid and generic pathway towards zero-carbon energy futures within the Sunbelt., Comment: Here is a summary of the study: https://www.dropbox.com/s/uvd90goh80y9eda/Zero-carbon%20Australia.pdf?dl=0

The efficient use of energy can reduce energy demand, improve energy security and economic competitiveness, and reduce environmental degradation. Using data from the United Nations Statistics Division, the paper analyses the trends in energy efficiency across countries in South Asia (SA) and identifies the determinants of the same. Unlike other studies in the context of SA, this study considers energy consumption of all energy-types taking place in all sectors of the economy. The study applies Logarithmic Mean Divisia Index-I to decompose the change in energy consumption during 1990–2014 into activity, structure, and intensity effects. The results show that the expansion in economic activities mainly drives energy consumption, and the shift toward energy intensive sectors further augments it. However, the decline in energy intensity restrains energy consumption in all the South Asian countries barring Nepal and Bhutan. The Composite Energy Intensity Index and the Fisher Ideal Intensity Index are also estimated to check the robustness of these results. The estimation of a panel regression model identifies endowment of energy resources, production of renewable energy, crude price, population density and GDP per capita as significant factors influencing energy efficiency in SA. These results have significant implications for energy use policy.

Environmental efficiency (EE) assessment is an efficient way to evaluate the degree of coordination between economy and environment. Most of the studies measured country- or region-level EEs, while the EE disparities among cities were not well investigated. By incorporating the socioeconomic and remote sensing data, this study measured the static and dynamic EEs of 11 provinces and 131 cities in the Yangtze River Economic Zone (YREZ) in China based on a super efficiency data envelopment analysis (SEDEA) and Malmquist index (MI) methods during 2003–2014. The influential factors of EE imbalance in the YREZ area were explored by the panel tobit model. Results show that large gaps exist in city's environmental efficiency. Cities in the Yangtze River Delta (YRD) show higher EEs than that in the Chengyu Urban Agglomeration (CUA) and Urban Agglomeration in the Middle Reaches of the Yangtze River (UAMR) areas. 15 cities have an EE below 0.2 and only 2 cities above 1 in 2014. The overall average EE exhibited a declining trend during 2003–2014. The number of cities below the average environmental efficiency increased from 70 (53.4%) to 83 (63.4%) over the time period studied. The MI results indicate that management and scale optimization level is the main factor hindering total factor productivity (TFP) growth. The tobit experiment reveals that GDP per capita played a negative impact on EE for most of the YREZ area during 2003–2014. The degree of opening up and industrial structure acted positively on city's environmental efficiency. These conclusions may be a helpful reference for decision makers to coordinate the economy and environment in the YREZ area.