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This paper investigates the impacts of coal prices on Chinese financial institutions' performance through the CAMEL indicators. We also analyze the potential impact mechanism of coal price shocks. Considering the energy consumption structure in China, we further discuss the impacts of electricity price control on coal price shocks. Results show that the coal price shocks have adverse impacts on Chinese financial institutions. And different industries play different roles in the transmission process of coal price shocks. Coal price shocks are partly mitigated by electricity price control, but these interventions in the market are inefficient.

Improving energy and carbon emission performance is of practical significance to the green economy, and Internet development may help achieve this goal. However, the effect of the Internet on energy and carbon emission performance has not been systematically explained. Based on Chinese provincial data from 2006 to 2017, this paper uses the non-radial directional distance function (NDDF) to measure energy and carbon emission performance and then constructs a two-way fixed-effect model. The results indicate that: (1) Internet development significantly improves energy and carbon emission performance, but there is significant spatiotemporal heterogeneity. (2) The analysis of the influence mechanism shows that Internet development is mainly to improve energy and carbon emission performance by promoting industrial structure upgrading and technology diffusion. (3) local government intervention weakens the effect of the Internet, while market-oriented development is conducive to the positive role of Internet. This study helps policy-makers better understand the impact of the Internet on energy and the environment, and formulate effective policies to achieve energy conservation and emission reduction.

We develop a heterogeneous autoregressive model of downside volatility (HAR‐DV) model with structural changes (HAR‐DV‐SC) model to investigate the effects of structural changes on predicting downside volatility. Then we employ HAR‐DV and HAR‐DV‐SC models to forecast downside volatilities in S&P 500 index, crude oil, gold, copper, and soybean futures markets. The in‐sample analysis shows that structural changes contain in‐sample information for predicting downside volatility. The out‐of‐sample analysis indicates that the HAR‐DV‐SC model outperforms the HAR‐DV model, suggesting that structural changes contain incremental out‐of‐sample information on future downside volatility. These results are robust and have important implications for risk management of stakeholders.

The rapid and massive process of urbanization in China has made the energy consumption of the residential sector an important motivator in national energy consumption. As the most important part of household energy consumption, electricity and its demand-side management play fundamental roles when exploring the methods of energy reform in China. Based on the framework of household production theory, this paper estimates the elasticities of Chinese residential electricity from 2006 to 2016 by using the data of 286 cities, and the first difference generalized method of moment estimator is employed. The results show that the demand for residential electricity is inelastic to price and is mainly affected by total expenditure, price of appliance and temperature. We also find that factors impacting electricity consumption vary among regions, and the demand is elastic to price change with the samples of the lowest domestic product. With the elasticity we estimated, a Ramsey tariff is calculated and compared with the current tariff, and the subsidy for residential electricity consumption is also analyzed. Based on the results, we propose to raise the price appropriately, and suggest the implementation of diversified pricing schemes according to regional characteristics to control the consumption by realizing the price mechanism, and to promote energy-saving and emission reduction in the residential sector.

Coal as the main fuel for the heating industry exerts heavy burdens on China’s environment. Based on meta-frontier non-radial distance function, this paper estimates the total-factor unified integrated efficiency of the thermoelectric industry in 29 provinces and analyzes its dynamic changes using Malmquist index. Besides, the composition of the meta-frontier Malmquist unified integrated efficiency is further evaluated to analyze the spatial-temporal features of the thermoelectric industry. The results show that the average total-factor unified integrated efficiency of the thermoelectric industry in the northern and southern China is 0.572 and 0.587, respectively, over 1999–2014. Although the total-factor unified integrated efficiency of the thermoelectric industry tends to improve, technical progress plays different role in the north and the south. Improvement in the thermoelectric industry in the north mainly relies on technical efficiency, while improvement in the south mainly relies on technical progress. Technological catch-up in both the north and south is weak.

Energy intensity and elasticity, together with inter-fuel substitution are key issues in the current development stage of Ghana. Translog production and ridge regression are applied for studying these issues with a data range of 2000–2015. The current energy dynamics reveal the expected inverse relationship: higher energy intensity and lower elasticity with economic growth. There are evidences of energy-economic challenges: high energy cost, inefficiency and backfire rebound effect. The implications are higher energy losses in the system, more consumption of lower-quality energy together with low energy technology innovation. Energy is wasted and directly not productive with economic activities. It is observed further that the higher energy intensity invariably increases CO2 emission because approximately 95% of total energy is derived from hydrocarbons and biomass. An inter-fuel substitution future scenario design was further conducted and the results were positive with growth, lower energy intensity, and improved energy efficiency. Therefore, government and energy policymakers should improve energy efficiency, cost, and productiveness. That is, they should change energy compositions and augment energy technology innovation, thus, increasing renewable share to 15% by 2026, reducing wood and charcoal by about 69%, and increasing natural gas to about 776%. Energy policymakers should enhance the installation of smart energy, cloud energy solution, tokenization of energy system and storage.

Whether the local economy can benefit from nuclear power plants will not only influence policymakers’ decision, but also the residents’ attitude. It is of great practical and academic value to scie...

China is the largest consumer of water resources in the world, the total consumption of water resources is still increasing year by year. What are the main reasons for rising water resource consumption? This paper constructs China's spatio-temporal LMDI (the Logarithmic Mean Divisia Index) model to decompose water resources consumption into twelve driving factors with panel data from 2000 to 2015 and explores the main factors driving the rising water resource consumption. The results are summarized as following: (1) The intensity effect is the most important driving factor decreasing water resources consumption; (2) The loss of farmers reduces the water resources consumption in the agricultural sector, and the increase of urban population drives the rising water resource consumption in the residential sector; (3) The effect of industrial structure is different depending on regions; (4) In the agricultural sector, the driving factors have their own characteristics in each region.

Renewable energy is not only an efficient way to ensure energy independence and security but also supports the transition to a low carbon economy and society. The progress of renewable energy technological innovation is an important factor that influences the development of renewable energy. An in-depth analysis of the driving factors that influence this progress is crucial to China's energy transition. Based on Chinese provincial data over 2000-2015 and panel data models, this paper regards the CO2 emissions as climate change and explores the response of renewable energy technological innovation to intensive CO2 emissions. We also analyze the effect of the driving factors such as energy price and R&D investment on this innovation process. The main conclusions drawn are: (1) There are significant differences in technological innovation levels across China's provinces. (2) We observe that the intensive CO2 emissions have promoted renewable energy technological innovation level, meaning that innovation process responds actively to climate changes. (3) R&D investment from government and enterprise both are conducive for promoting the innovation level. (4) Energy price has an insignificant effect on innovation in renewable energy technologies and we attribute this to the unreasonable energy price mechanism. This paper provides clear evidence for understanding the role of innovation on climate change.

Can Ghana build its economy on renewable energy? Is it possible to substitute nonrenewable for renewable energy? What is the impact of renewable energy on the economy of Ghana? These questions are relevant and worth considering as far as the renewable energy development agenda in Ghana is concern. Using the translog production function approach, this study primarily investigates whether Ghana can move away from nonrenewable electricity generation to renewable power generation. In particular, we provide estimates for the relationship between the two energy types and output. The estimates are then used to calculate the output and substitution elasticities between renewable and nonrenewable energy. Our findings show that while the impact of nonrenewable energy on Ghana's economy is negative, that of renewable energy is insignificant, with output elasticities for both energy types averaging −0.139 and 1.875 respectively. Even though substitution elasticity suggests the possibility of moving towards a renewable power generation system, this study also highlights the limitations associated with such transition, citing inherent problems such as scale, cost, and location. Based on the analysis done in the context of the findings, this study recommends a policy that supports an optimal energy matrix with both renewable and nonrenewable sources.

Since the metallurgical industry has become the main source of China's carbon dioxide emissions and energy consumption in recent years, low-carbon transition in that industry is of great significance for achieving China's carbon reduction targets. It is generally believed that phasing out fossil fuel subsidies is an effective way to reduce energy-related CO2 emissions since it can increase the energy prices and lower its consumption. This paper aims to investigate whether the energy subsidy removal can promote the low-carbon transition of China's metallurgical industry. Taking inter-fuel and inter-factor substitution effects as the link, we calculate the CO2 mitigation potential on the assumption that the subsidies for each category of fossil energy were eliminated. We find that the metallurgical industry has a sluggish reaction to the changes in energy price. Supposing eliminating the energy subsidies in the period of 2003–2015, the amount of reduced CO2 would be 487.286 million tons, accounting for a slight proportion of the total emissions in the industry. But it is meaningful for the global CO2 mitigation since it approximates the whole CO2 emissions in Norway during the same period. These findings can provide some new insights for the energy subsidy issue and suggest that the additional measures are required to promote the low-carbon transition in China's metallurgical industry rather than just relying on the removal of fossil fuel subsidies.

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.

China's manufacturing industry consumes more energy than the total amounts of Germany, Britain, France, Spain and Japan combined, and has a substantial impact on energy conservation and emissions reduction. This paper investigates the influence mechanism of economic infrastructure on the energy consumption and energy intensity of the sector. Using the China's provincial data during the period 2003–2016, the profit function is applied to incorporate infrastructure into the input-output system, and to avoid the endogenous problems caused by the reverse causal relationship between energy consumption and infrastructure construction. The empirical results indicate that economic infrastructure construction will increase energy consumption and reduce the energy intensity in the long term. Although the energy consumption effect of infrastructure in the central and western regions is less than that in the eastern region, the energy intensity of the western region declines the most due to the infrastructure construction. Based on the results of this paper, some policy implications are discussed. This paper also offers some targeted policy recommendations to improve policy design of the government.

China has the largest electricity system in the world, which is undergoing a critical market reform. Based on the panel data in 2000 to 2016 of China, this paper tries to explain theinconsistency between electricity consumption and economic growth. For example, in 2015, GDP growth rate was close to 6.9% but with only 2.9% growth in electricity consumption. However, in 2018, a 6.6% growth in GDP required an 8.5% electricity growth. The results explain the reasons for inconsistency and identify the key factors. We find that the increase in inventory, fixed capital and industrial electricity consumption will narrow the gap when economy grows faster than electricity consumption, and widen the gap on the contrary. Heterogeneity exists among regions, the model and variables seem most convincing in explaining the situation in the western region, which is most similar to the full sample of China. Feedback effect is also observed between electricity consumption and economic growth in most parts of China by Granger test. The findings provides suggestions for the policy makers in making power planning, in an effort to avoid large power shortage or surplus.

Data envelopment analysis (DEA) and the Biennial Malmquist–Luenberger (BML) productivity index are applied to measure and decompose traditional energy efficiency under the carbon emission constraints of China's textile industry for the period 2000–2012. It is found that: (1) overall, the average growth rates of technical improvement, labor, capital for energy substitution, and output structural change are positive, indicating that these are the main factors that improve energy productivity, with output structural change making the largest contribution; (2) the average growth rates of the energy structure effect and technical efficiency are negative, which indicates that the energy structure effect is the main factor hindering energy productivity growth and that the deterioration of the technical efficiency change can inhibit this growth; (3) energy productivity is generally higher in eastern China than in central and western China; and, (4) in the decomposition of energy efficiency, the difference in energy efficiency between eastern and western China is mainly due to structural effects.

The international community generally agrees that renewable energy, such as wind power, is conducive for achieving CO2 mitigation, environmental protection, energy savings, and energy security. The innovation in wind power technologies is vital to achieving a transformation of energy structure. The study sought to investigate the effects of policies (feed-in tariffs and research and development spending), their interaction, wind power deployment and electricity prices on wind power technology innovation at the provincial level in China, based on negative binomial fixed effect regression model and provincial panel data from 2006 to 2016. The following conclusions are drawn from the findings: (1) demand-pull policies through feed-in tariff policy promote innovation in wind power technologies; (2) higher feed-in tariffs of wind power induce greater stock of patents in wind technologies; (3) technology-push policies through research and development spending support wind power technological innovation; (4) only research and development funding investment in industrial enterprises under the implementation of feed-in tariff policy can stimulate greater patent stock due to the fact that the wind power industry enterprises are gainers for feed-in tariff policy, indicating that there is an interaction effect; (5) improving the wind power deployment drives wind power technology patents; (6) increasing electricity prices will incentivize innovation of wind power manufacturers in order to reduce cost and obtain more profit.

China is now the world's largest emitter of carbon dioxide (CO2), thus leading to China facing enormous pressure on CO2 emission reduction. Natural gas is a high thermal and low-emission energy. Expanding natural gas consumption cannot only meet the growing demand for energy consumption but also optimize energy consumption structure. Therefore, many scholars have investigated the effect of natural gas consumption on CO2 emissions. However, ignoring a large number of nonlinear relationships between economic variables, the vast majority of existing studies use traditional linear models to explore the relationships between natural gas consumption and CO2 emissions. In order to make up for the gap in existing research, this paper uses the nonparametric additive regression model with data-driven features to investigate the relationships between the two. The results show that natural gas consumption has an inverted “U-shaped” nonlinear effect on CO2 emissions in the eastern region, but a positive “U-shaped” nonlinear effect in the central and western regions. The linear impact of natural gas consumption on CO2 emissions in the eastern and central regions is higher than that in the western region, due to the differences in resource availability and energy prices, as well as natural gas consumption. Therefore, during the process of promoting natural gas consumption, the central and local governments should adopt heterogeneous measures at different stages of development.

As one of the energy-intensive industries, the CO2 emissions of China's petroleum refining and coking industry (PRCI) put significant pressure on China's emission reduction target. Considering this, the main purpose of this paper is to clarify the determinants of emission growth in China's PRCI and predict the emissions change trend in future. Based on an extended Kaya identity, this paper dynamically analyzed the impacts of investment scale, investment efficiency, energy intensity, energy structure and carbon dioxide emission factor on CO2 emissions from China's PRCI. Then, the possible CO2 emissions of China's PRCI in 2030 were dynamically predicted based on the application of probabilistic scenario analysis. The main findings include: (1) during 2000–2016, China's PRCI suffered 130.82 million tons CO2 emissions growth, investment scale expansion was the principal contributor stimulating CO2 emissions. Fortunately, the promoting effects exerted from this factor showed a downtrend along with the decreasing fixed-asset investment growth rate since China's economy has entered a new normal. (2) During the entire study period, energy structure adjustment brought about a mild increase in CO2 emissions rather than taking an active role in emission abatement. (3) Investment efficiency and energy intensity were two crucial inhibitors, followed by the carbon dioxide emission factor. However, the inhibiting effects resulting from these factors cannot completely counteract the promoting effects in most cases. (4). Scenario analysis indicates that the mitigation pathway of CO2 emissions in China's PRCI include properly curbing the fixed-asset investment, increasing the green investment for technology and equipment, paying more attention to the R&D of energy saving technology in PRCI and its downstream industry, and facilitating the use of clean electricity.

Although China's food industry is not energy-intensive, its rapid development has brought enormous challenges to the realization of the energy-saving goal due to its huge scale. So the industry must reduce its energy intensity as much as possible. This paper uses a comprehensive decomposition analysis to study the energy intensity changes in China's food industry between 2000 and 2013, and draws the following conclusions: (1) technological progress have the most significant impact on reducing energy intensity, reducing it by 70.2%; (2) technical efficiency change hinders the energy intensity decline, which in turn increases it by 18.4%; (3) the substitution of capital for energy is conducive to the decline of energy intensity; (4) the government's industrial policy has played a role in energy conservation, but there is room for improvement. Therefore, this paper suggests that: Firstly, the R&D investment in production and management technology should continue to increase, and more importance should be attached to technology diffusion and transfer between regions. Secondly, more effective incentives and stricter constraints should be implemented for the food industry. Thirdly, the planning of the industry layout should be adapted to regional advantages, and targeted policies should be carried out in different regions.

CO2 (carbon dioxide) emissions from fossil fuels have turned out one of the most perilous and complex issues driving the climate change debate. The issue of increasing demand for electricity at the same time reducing CO2 emissions has become important worldwide. This study evaluates the CO2 emissions from the power sector of Pakistan during 1978–2017 using Logarithmic Mean Divisia Index technique and scenario analysis. We estimate LMDI to check the change under prearranged elements. According to results, the shift in population, activity effects, and gross domestic product are the essential factors in charge of the increase in CO2 emissions while carbon intensity and energy intensity effects participate in cutting emissions. Finally, scenario analysis found overall activity increases due to population and intensity of CO2 emissions, but carbon intensity could be reduced by using cleaner energy technologies. The findings suggest that CO2 emissions will possibly arrive at 277.9 Mt in 2035 in Pakistan. In relation with the main findings, some targeted policies suggestions are provided.

To develop renewable energy as well as promote China's transition to a low-carbon economy, the government needs to pay attention to renewable energy technological innovation (RETI). Using China's provincial panel data from 2000 to 2015, and regarding the CO2 emissions as the proxy of climate change, this paper identifies the relationship between RETI and CO2 emissions as well as seeks to confirm the role of RETI on climate change. The linear regression model confirms that the RETI has a significant negative effect on CO2 emissions. In addition, considering the disparities of energy structure, the impacts of RETI on CO2 emissions may be distinct. We, therefore, construct a panel threshold model by taking into account the distinct effect of RETI under different energy structure. We find that the effect of RETI on curbing CO2 emissions decreases with the rising of coal-dominated energy consumption structure but in contrast, this effect increases with the growing proportion of renewable energy generation. This paper provides new insight into the relationship between technological innovation and climate change. Based on these findings, some relevant policy recommendations are proposed.

Chinese energy consumption structure has been dominated by coal for a long time, which makes China facing serious environmental problems. To achieve the target of CO2 emission reduction, a carbon emission trading (CET) market and promoting the development of new energy should be worthy choices. The issues of dynamic linkages and spillover effects between the CET market, the coal market and the stock market of new energy companies (NEC) are important topics for studies. The paper applies a VAR(1)-DCC-GARCH(1,1) model and a VAR(1)-BEKK-AGARCH(1,1) model to obtain the following conclusions: (1) There exists significant time-varying correlations and long-run persistence of shocks to the DCCs between Beijing CET market, the coal market and the stock market of NEC, and the coal market and the new energy stock market have higher volatility persistence; (2) There is a bi-directional spillover effects between the coal market and the stock market of NEC, which is consistent with the results of the Granger causality; (3) The optimal weight is 82.65% for new energy stock and 17.35% for coal. The optimal hedge ratio is 49.98% for coal, while the optimal hedge ratio is 17.34% for new energy stock.

Compared with developed countries, capital markets and financial systems in developing countries are often labeled as underdeveloped. Firms need funds to operate and to invest for pollution abatement. Private, small and medium-sized enterprises (SMEs) generally face financing constraints in China, which may be closely related to poor environmental performance in recent years. Based on World Bank survey data from 2011 to 2013, controlling location area, industrial sector, firm scale, and ownership, the influence of financing constraints on firm's environmental performance is examined. It is found that minor obstacles in financing activities don't seem to impair firm's environmental performance. However, moderate and major obstacles undermine firm's environmental performance with high investment costs and biased incentives. The more severe the credit constraint, the higher probability of poor environmental performance is observed. In the end, targeted policy implications with emphasis to provide financing convenience for SMEs are addressed.

Municipal solid waste in China has increased rapidly in recent years with urbanization and economic development. The establishment of affordable, effective and truly sustainable waste management is a key point for the development of urban areas. However, as a renewable energy, the cost-benefit of supporting and the environmental performance for waste incineration power are seldom discussed. This paper discusses the current situation of waste incineration power industry in China and analyzes the required subsidies and emission reductions. The results show that waste incineration power needs relatively less financial support and perform well on emission reduction. Main obstacles to the development of waste incineration have also been investigated from the perspective of government, enterprises and local residents. Lastly, some suggestions are offered on how to tackle these issues. For instance: more regulation on waste classification; to integrate the informal workers into formal municipal waste management system; to communicate more effectively to the public to dispel general misunderstanding; application of the waste heat from cogeneration plants to local heat supply or central heat supply system.

Based on provincial panel data on the textile industry in China, this paper calculates the total-factor energy efficiency of this industry as the dependent variable. Additionally, based on linear panel analysis of the relationship between industrial agglomeration and energy efficiency, in-depth analysis of the industry is performed at different industrial agglomeration levels. The paper identifies different impacts of industrial agglomeration on energy efficiency, uses the threshold regression model to extend the research to a nonlinear framework, and constructs a double threshold regression model in which the threshold of the textile industry agglomeration level serves as the threshold variable. The results show, first, a threshold effect occurs when industrial agglomeration affects total-factor energy efficiency. Second, a significant positive correlation exists among the degree of economic development, energy prices, research and development investment (R&D), enterprise scale, and total factor energy efficiency of the textile industry. Third, a non-linear relationship exists between industrial agglomeration and energy efficiency in the industry. When industrial agglomeration is low, promoting it improves energy efficiency. However, when industrial agglomeration reaches a certain level, agglomeration and energy efficiency show a negative relationship. Finally, based on the empirical results, ways of improving energy efficiency in the industry are suggested.

Using the trans-log cost function and elasticity model, this paper pioneered the analysis of the elasticity and characteristics of the substitution between energy consumption and capital in China's manufacturing industry between 1995 and 2014. Firstly, the research revealed significant differences in the magnitude and direction of the substitution between coal, electricity, petroleum, and capital, thus demonstrating heterogeneity in the substitution. Secondly, the analysis, based on the Morishima Elasticity of Substitution (MES) model showed that: 1) depending on what triggers changes in factor prices, the manufacturing sector shows a reverse asymmetry whereby capital substituted energy and energy complemented capital 2) a significant asymmetry exists in the direction and magnitude of substitution in capital- and labor-intensive sectors. This asymmetric effect indicates that China can attain overall energy conservation and apply energy-saving technologies in the manufacturing industry by implementing the substitution of capital for energy. However, the differences with regards to the forms of energy consumption among sectors, as well as the heterogeneity and asymmetry in capital–energy substitution should be considered in research on energy policies for the manufacturing industry. This should also involve the integration of energy sources to ensure that such policies are targeted and effective.

Modeling volatility with reasonable accuracy is essential in asset allocation, asset pricing, and risk management. In this paper we use the ensemble empirical mode decomposition method and Zhang et al. (2008, 2009)’s method to decompose realized volatility into different volatility components. Then, we propose two new heterogeneous autoregressive (HAR) models by combining with the volatility components and leverage effect. Finally, we use high-frequency data for the S&P 500 as the study sample and perform parameter estimations on eight HAR-type models (including two new models). The results indicate that our models that are used to model 1-day, 1-week and 1-month future volatilities have an advantage over other existing HAR-type models. This advantage is substantial in the case of 1-month future volatility. In addition, the leverage contains significant in-sample prediction information for future volatility.

Though the development of renewable energy is rapid, innovation in renewable energy technologies is relatively weak due to the late commencement of renewable energy in China. In addition, renewable energy is mainly introduced into the supply mix of electricity generation, which increases the costs of electricity generation. Higher electricity price will make renewable energy more competitive and call forth renewable energy technological innovation. Based on FMOLS and DOLS models, as well as PMG model, this paper investigates the induced long and short run effects of electricity price, funding support, and economic growth on innovation in renewable energy technologies at the provincial level in China during the period 2006–2016. The Conclusions drawn were: (1) RD (2) Electricity price only has a long run effect on patenting in renewable energy technologies; (3) In the long run, a 1% increase in electricity price can lead to a 0.7825%–1.0952% increase in the patent counts of renewable energy technologies; (4) Electricity pricing system in China does not play any role in driving renewable energy technological innovation in the short run.

On December 29, 2017, China's Carbon Trading Scheme (ETS) was officially launched, and it may be the largest emission trading platform in the world. This paper establishes 5 counter-measured scenarios based on the recently launched China's national ETS market and constructs a dynamic recursive Computable General Equilibrium model to study the impact of national ETS on the economy, energy, and environment. We find that the national ETS will have a negative impact on GDP by 0.19%–1.44%. The national ETS can significantly increase the price of electricity, however, the increase in the prices of other commodities will be much lower than that of electricity. As long as the mechanism of the ETS market remains unchanged, emission reduction per year will increase linearly. Economic output and CO2 emission are sensitive to Annual Decline factor (ADF). This paper argues that China's national ETS market is an effective tool to reduce CO2 emission, and we suggest that ADF could be 0.5% when allocating carbon allowance for the electricity sector. This could balance economic output and CO2 reduction. Also, it is easy to achieve the goal of “double control” (total amount and intensity) in China.

Considering the objectives of Nigeria's Renewable Energy Program, three basic questions are asked in this study: Can Nigeria really build its economy on renewable energy? Is it possible to substitute nonrenewable energy for renewable energy? What is the impact of renewable energy on the economy? This study primarily analyzes renewable energy development in Nigeria. Among other things, we estimate the economic impact, output elasticity, and the substitution possibilities of both renewable and nonrenewable energies. Our findings, based on a dataset from 1980 to 2015, in the framework of the translog production function, show that capital and labor are the main drivers of output in Nigeria; however, the economic impact of both energy types (renewable and nonrenewable) are insignificant, even though positive. With output and substitution elasticities being positive, this study confirms the possibility of moving towards a renewable power generation, but also highlights the limitation associated with such transition, citing inherent problems such as power density and scale, cost, and location. Based on Nigeria's economic and industrialization agenda, this study agrees with the respective strategies enshrined in the Renewable Energy Program but recommends that its implementation should be gradual and in accordance with Nigeria's economic goals.

With the increase of renewable energy permeability and the development of distributed grid, energy storage plays an increasingly important role in the power system. A lot of studies have shown that energy storage can already be economically feasible. However, most previous studies concentrated on the value of energy storage in the free electricity market. In China, the power grid monopolizes the process of electricity transmission, distribution and retail, and the feed-in tariff and retail prices of electricity are regulated by government. It is difficult to analyze the application value of energy storage for China's electricity due to the lacking of data. The major contribution of this paper is to evaluate the application value according to the data of a provincial power grid. The results support the argument that energy storage can generate positive returns. The optimal storage capacity and operational strategy are also discussed in this paper.

Due to the ongoing structural transformation in China, the service sector now constitutes approximately half of the total Chinese output. At the same time, the internal structure of this sector continues to change rapidly with the level of Chinese development. We analyze the impact of changes in the structure of Chinese service sector on the unified efficiency of Chinese service sector considering the energy consumption and carbon dioxide emissions. The non-radial directional distance function and the global data envelopment analysis method are combined to measure the unified (technical, allocative, operational and environmental) efficiency of Chinese service sector at both regional and sectoral levels. The results indicate that the financial sector and the sector of transportation, storage and post are the industries with the highest and lowest unified efficiency respectively. There exist differences across industries and regions in the unified efficiency of the service sector. As a second stage, a panel Tobit regression is processed to examine the effect of service structure on the unified efficiency of the service sector, in which a significant impact appears to exist. Promoting economic development and enhancing investment in research and development are beneficial to improving the unified efficiency of the service sector.

In 2020, the world experienced several significant events, including the COVID-19 pandemic and the collapse of international crude oil prices. Both have a great impact on a sustainable economy. Taking China as an example, we use a computable general equilibrium model with multi-sectors and multi-households and consider six different scenarios to simulate and evaluate the aggregate impacts of the pandemic and crude oil prices. We divide the impact of the pandemic into the changes of factor input and the changes of consumer preference and find that the decline of factor input is the leading cause of the economic downturn. The sharp drop in crude oil prices has a significant negative impact on the low-carbon economy. Although the pandemic has led to a decline in global carbon emissions, it is only because of the economic downturn. The epidemic situation and the change of oil price have double impacts on the economy, especially the sustainable economy. Adjusting the price gap between fossil energy and renewable energy (e.g., more stringent carbon pricing) and appropriate tax cuts on residents may be effective ways to alleviate the impact, which should be one of the environmental policies in the post-COVID-19 era.

China has promised that by 2030, its carbon intensity will be cut down by about 60%. Unlike existing related research, this article employs a novel semiparametric regression model to explore the carbon intensity of the heavy industry. The main results are as follows: (1) the relationship between spatial agglomeration and carbon intensity shows a non-linear pattern, mainly caused by the cross-regional flow of production factors. (2) The impact of technological progress on carbon intensity also displays significant non-linear characteristics, mainly due to the staged differences in R&D investment. The following three factors are linearly related to carbon intensity. (1) Energy consumption structure has a more significant linear impact on the carbon intensity in the central and eastern regions, due to the heavy coal consumption of the heavy industry in these two regions. (2) The linear impact of environmental regulations on the carbon intensity in the eastern region is more significant, owing to the region’s considerable investments in environmental governance. (3) The industrial structure has the most significant linear impact on the carbon intensity in the eastern region, since the scale of the tertiary industry in this region is larger.

China's demand for paraxylene, an essential material for the chemical industry, is growing rapidly. However, the public is worried about the influence of paraxylene plants on the environment. The contradiction between supporting paraxylene and opposing it poses a considerable challenge for the Chinese government. Therefore, objective evaluation of paraxylene plants' impact on the environment is of great significance to industrial development. Using the urban panel data of 102 cities in China from 2003 to 2017, this paper applies the PSM-DID method to study the relationship between paraxylene plants and air quality. This paper shows that paraxylene plants play a significantly positive role in aggravating urban air pollution. Specifically, paraxylene plants increase not only industrial dust pollution but also increase SO2 emissions. This influence is still valid after multiple robustness tests. Moreover, the mechanism analysis reveals that paraxylene plants' construction promotes income growth but hinders foreign direct investment. Finally, this paper provides policy suggestions on promoting paraxylene industrial development in terms of governments, enterprises, and the public.

There is a consensus within the international community that replacing traditional fossil energy with renewable energy, such as photovoltaic energy, will help mitigate climate change. However, the literature addressing the rapid development issues of the photovoltaic industry and related carbon dioxide abatement costs is limited. China is currently the largest photovoltaic producer and consumer in the world, hence suitable as our research object. In this paper, a fixed effect panel model with provincial panel data during the period 2012–2016 is applied to study the factors that influence China’s photovoltaic industry. The empirical results indicate that carbon dioxide emission mitigation requirements, government subsidies, technological progress, energy substitution, economic growth, and illumination resources promote the development of the photovoltaic industry. We further adapt the cost estimation model to estimate the average carbon dioxide abatement cost of photovoltaic electric power in China at 679.72 yuan/ton in 2015 and 681.88 yuan/ton in 2016. Compared with wind power and biomass energy, photovoltaic electric power is currently less economical for carbon dioxide emission reduction. Moreover, the future carbon dioxide abatement cost is predicted using a scenario analysis at 118.94–259.42 yuan/ton in 2025 and 42.63–171.95 yuan/ton in 2030. Since the carbon dioxide abatement cost in 2030 is in line with the future price level of the carbon trading market, it will be both economical and feasible to use photovoltaic electric power to decrease carbon dioxide emissions in the future.

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.

Coal accounts for more than 60% of China’s primary energy consumption. Due to the demand decline since 2013, the coal industry was facing the dilemmas of falling prices, overcapacity, and high debt ratios. Reduction of overcapacity of the coal industry has become a crucial task in China’s supply-side structural reform. This paper attempts to explain several issues related to overcapacity reduction in the coal industry. First, we analyze the characteristics of China’s coal market and the causes of over-capacity in the coal industry. It is revealed that the aggregate coal demand of China is price inelastic, and the coal enterprises own market power. In addition, we illustrate that current overcapacity is the result of enterprises’ rational expansion in the context of rapid growth in demand in the previous period. Second, different capacity reduction schemes are compared. The results suggest that some of the inefficient production capacity should be temporarily withdrawn from the market, rather than ordering all coal mine to limit production capacity in the same proportion. Third, we conduct a regression model to describe the long-term price trend of coal and establish a mean-reverting model to simulate the motion path of the coal price. According to the Monte Carlo simulation, we estimate the value of the real option of coal capacity and find it is higher than the capacity replacement cost. This demonstrates that the real option is economically feasible in application.

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.

Compared with wind and solar power, biomass power has grown relatively slowly in China. With abundant biomass resources, the development of biomass electric power in China has potential advantages. This paper analyzed the environmental impact of biomass power in the construction and operation stages in comparison with wind and solar power. The results showed that biomass power produced relatively less emissions in the system construction stage at around 1700 ton CO2-e/MW. In the operation stage, biomass power projects achieved an average of 131462 ton CO2-e per year, which is greater than wind and solar power of equal installed capacity. Biomass power plants could achieve net emission reductions in a shorter time (0.39 year) after operation. The life cycle GHG emissions of biomass power projects are between 42 and 85 g CO2-e/kWh. The evidence pointed out that biomass power is worth supporting in China, from the perspective of environmental performance. Local governments should promote the sustainable supply of biomass materials and the development of renewable energy industry should depend on local conditions.

Rapid development of agriculture mechanization and the agro–industry in China (considered as a large agricultural country) has led to a substantial increase in energy consumption and CO2 emissions. Majority of existing studies usually explore the driving forces of the sector's CO2 emissions using the averaging method. However, data distribution of economic variables is often non–normal, with the tail having hidden important information. According to the average annual CO2 emissions, this paper divides China's 30 provinces into six quantile grades, and uses the quantile regression method to investigate the driving forces of CO2 emissions under high, medium, and low emission levels. The results show that the effects of economic growth on CO2 emissions in the upper 90th and 75th–90th quantile provinces are higher than in the 50th–75th, 25th–50th, 10th–25th and lower 10th quantile provinces due to the differences in fixed–asset investment and agricultural processing. The impact of energy efficiency in the upper 90th, 75th–90th, and 50th–75th quantile provinces are stronger than those in the 25th–50th, 10th–25th, and lower 10th quantile provinces because of the huge difference in R&D funding and R&D personnel investments. The effect of urbanization in the higher 90th quantile provinces is higher than in the other quantile provinces, owing to the differences in the level of agricultural mechanization and human capital accumulation. Similarly, financial capacity has the largest impact on CO2 emissions in the upper 90th quantile provinces in all quantile provinces. However, the impact of industrialization in the upper 90th quantile provinces is lower than in other quantile provinces. Thus, the heterogeneous effects of the driving forces should be taken into consideration when discussing CO2 emissions reduction in China's agriculture sector.

China has the largest textile industry with the complete industrial chain and also the largest textile exporter in the world. The study analyses the energy substitution effect of technological progress of China's textile industry using a macroeconomics approach. In order to predict future energy saving potential, we examine the relationship between energy intensity and its five main factors (technological progress, enterprise scale, labor productivity, dependence on foreign trade and industrial electricity price) by co-integration technique. Empirical results indict that electricity shows alternative features to other energy sources in the context of technological progress in China's textile industry. Besides, there exists a long-run equilibrium among energy intensity and the five main factors. Monte Carlo method was applied for risk analysis to ensure the reliability of forecast. Further, future energy saving potential and CO2 emission reduction of China's textile industry was predicted using scenario analysis. The result shows that energy conservation potential of China's textile industry is 16.16–27.53 million tons of standard coal equivalent in 2025. Additionally, it was revealed that the CO2 emission reduction caused by the energy conservation will be 32.63–55.60 million tons in 2025. Finally, future policy priorities for energy conservation of Chinese textile industry are suggested.

Countries all over the world have realized that the key to resolving the dilemma between accelerated energy consumption and reduction in carbon dioxide emission is to actively develop new energy industry. Many scholars have conducted in-depth investigation into the main driving forces of the new energy industry. However, they often adhere to an assumption that the effect of the driving forces on new energy industry is constant across areas, ignoring the spatial heterogeneity in economic phenomena. Geographically weighted regression (GWR) model and the use of local sample data to implement parameter estimation can make up for the inadequacies of existing research. Therefore, this paper uses the GWR model to carefully investigate the new energy industry. The results show that the impact of economic growth on the new energy industry in the eastern region is higher than in the central and western regions owing to the differences in economic structure and fixed asset investment. The impact of foreign energy dependence continuously declines from the eastern region to the central and western regions. This is attributable to the differences in natural gas and oil imports. Technological progress has a similar effect on account of the differences in R&D funding and R&D personnel investments. However, the impact of the agriculture industry in the central region is higher than in the eastern and western regions due to the differences in crop acreage and agricultural output. This study improves our understanding of the new energy industry and would help local authorities to formulate targeted policies for promoting the growth of new energy industry.

This study first used principal component analysis to calculate an operating index for China's energy industry and applied the index to investigate cyclical fluctuation trends. Subsequently, a time-varying parameter structural vector auto-regressive model was adopted to analyse the time-varying characteristics of the cyclical fluctuations in the macro economy and carbon emissions. The results showed that cyclical changes in the energy industry lagged fluctuations in the macroeconomic environment. The average length of the cycle was three years. Industrial shocks had a clear pro-cyclical impact on production levels and prices. During periods of prosperous growth, these positive shocks can sustain increased output and price levels. During recessionary periods, positive shocks have a short-term effect and are not favourable for output growth or price rebounds in the long run. The macroeconomic effect of cyclical fluctuations in the energy industry clearly has a stronger price effect than output effect and a greater impact on producer price inflation than consumer price inflation. In addition, the effect of energy shocks on carbon emissions has gradually weakened over time. We argue that promoting supply-side structural reform policies and eliminating excessive energy capacity within the industry while continuing to promote green energy strategies are key solutions to reversing supply-demand imbalances and downward price trends.

China is currently the world's largest emitter of carbon dioxide (CO2). Vigorously developing new energy sources has become an important way to reduce CO2 emissions. Therefore, more and more scholars have studied effective ways to promote the development of new energy industry. However, most of the existing research use single-equation linear models or static models to study the driving forces of the new energy industry. This not only ignores the large number of dynamic relationships between economic variables, but also produces endogeneity problems. In order to overcome the shortcomings of existing research, this paper uses vector autoregressive model to study the new energy industry. The results show that energy consumption structure has a positive effect on the new energy industry in the short run, but the effect is limited in the long run. The impact of the agriculture industry is gradually narrowing over time due to the gradual reduction of crop acreage. However, the influence of economic growth is positive both in the short and long run. This is due to the gradual optimization of industrial structure. Technological progress produces a similar impact, owing to continued investment in research and development funding as well as research and development personnel.

Energy consumption propels economic growth but the level of CO2 emissions associated with a fossil-dominated energy structure raises concerns for the fight against climate change. To draw consensus, this study develops a translog-causality-based model in order to study causation between electricity, natural gas, petroleum, and economic growth in Egypt. In addition, the models’ results are used to estimate the substitution possibilities between various energy pairs and to subsequently test the CO2 mitigation benefits arising from fuel substitution. Results support a bidirectional relationship between all energy types and economic growth in Egypt and suggest, also, that these energy types are substitutes. Although technical progress is estimated to be a bit slow (varying between 4.5% and 7.5%), there appears to be substantial CO2 emissions mitigation benefits from fuel substitution amounting to reductions in the range of 1.5 and 2.2 million metric tons under a 5% investment scenario and 2.5 and 4.5 million metric tons under a 10% investment scenario. These results have broader implications for energy conservation policies and industrial merger policies in developing countries. Moreover, by studying technical change, insights are provided on future CO2 mitigation potential driven by energy efficiency.

Majority of the increase in global energy consumption is from China; hence, studying energy issues, especially in China’s manufacturing industry (CMI), is worthwhile and of much interest in the academic field. Based on the translog cost function, we develop a research framework to study the rebound effect of CMI. Considering the effect of asymmetric energy price, we augment the energy-cost function with asymmetric influence constraint of energy price. Again, we add time series data of CMI’s capital, labor, energy, and mid-input to the model to calculate the direct rebound effect of CMI. We find that the rebound effect of CMI is 44.2%, and CMI still has large energy-conservation potentials. Based on the results of this study, some policy recommendations are provided.

Guiding industries to produce in an effective and environmentally sustainable manner has become a key issue for governments around the world. Over the past 15 years, series of regional and industrial development policies have been introduced by the Chinese government. This has influenced the geographical distribution and energy efficiency performance of China’s industries. This paper quantifies the influence of enterprises geographical distribution on energy efficiency improvement in the paper industry and aims at providing some helpful suggestions on industrial development to policy makers. The main results show that, firstly, the paper industry in eastern China shows obvious characteristic of agglomeration. The average location quotient is 1.2278. However, following the industrial and regional development policies, the characteristic of agglomeration is weakening. Secondly, only when agglomeration reaches a certain level (location quotient is above 0.5447) will industrial agglomeration positively impact on industrial energy efficiency improvement (a 1% increase in agglomeration will increase dynamic energy efficiency by at least 0.23%). Thirdly, the regional development policies carried out by the government slow the pace of energy efficiency improvement in China’s paper industry. The empirical results indicate that the government needs to consider regional characteristics and consciously guide industrial enterprises to concentrate in the dominant area.

Forecasting total energy demand and its structure is the basis for energy planning and industrial policy formulation. However, existing research on the forecast of energy structure remains inadequate. This study aims at constructing an ADL–MIDAS model to identify the optimal model to forecast China's energy demand and its structure, and offer a reasonable judgement on future carbon emission and energy scenarios in China and other developing countries. Thus, this study adopts mixed frequency data for quarterly GDP, quarterly added value, and annual energy demand of various industries to construct an ADL–MIDAS model. Then, the optimal model to forecast China's energy demand is selected from various model combinations that employ different weight functions and forecasting methods. The model forecasts China's total energy demand and its structure as proposed in the 13th Five Year Plan. The in-sample prediction results show that, in the optimal model, the smallest prediction error is 0.02%, while the largest of the four future periods is 2%, indicating that the ADL–MIDAS model is effective in forecasting energy demand. Further, the forecast results suggest that, by 2020, China's total energy demand will reach approximately 4.65 billion tonnes of standard coal equivalent; the demand for coal, natural gas, and non-fossil fuel will be 57%, 7.6%, and 18%, respectively, contingent on economic growth conditions. Given these forecast results, the energy planning targets set under the 13th Five Year Plan are attainable. However, in the case of natural gas demand, considerable marketing is required to promote its use.

As a result of rapid development of industrialization and urbanization, energy (especially fossil fuels) demand growth is increasing. Hence, China is facing the huge pressure of environmental prote...