Regional Energy Intensity Research Articles

How digital technology affects regional energy intensity from the perspective of energy economy: a case study of the Yangtze River economic belt

How digital technology affects regional energy intensity from the perspective of energy economy: a case study of the Yangtze River economic belt

The Impact of R&D Expenditures on Regional Energy Intensity in Turkey

Energy efficiency measures are effective solutions for addressing environmental and economic issues, including global warming, energy supply security, and trade deficits from imported energy sources. Turkey through an ambitious policy scheme in the last decades, have reduced energy intensity level significantly, though, still higher than developed countries. In this regard, this study investigates the relationship between R&D expenditures and energy intensity in Turkey through NUTS-I regional data ranging from 2010 to 2021. According to both FMOLS and DOLS technique findings increasing R&D expenditures may significantly decreases energy intensity. Furthermore, economic growth appears to correlate with reduced energy intensity. Additionally, FMOLS findings suggest that industrialization may increases energy intensity, while DOLS results indicate that population growth can also significantly increase energy intensity. Therefore, a customized research and development (R&D) incentivization support system could offer various benefits to Turkey and other developing nations facing similar challenges.

Energy intensity convergence among Chinese provinces: a Theil index decomposition analysis

China, the world’s largest carbon emitter, has one of the most stringent provincial emissions reduction programs, incorporated into its Five-Year National Plan to reduce carbon emissions. However, the widening energy intensity gap between provinces poses a great challenge for carbon reduction. In this study, we analyze the convergence of Energy intensity (EIC), i.e., the time-dependent decrease of differences among regional energy intensity over time focusing on a data set of 30 Chinese provinces from 2000 to 2015. Our goal is to identify the provinces that are responsible for the observed divergence in energy intensity and identify the factors causing that divergence in each individual case. The Theil index is used to capture inter-provincial energy inequality. We use the LMDI decomposition analysis to identify the drivers of energy inequality (energy consumption structure, energy efficiency, and industrial structure). The results suggest that reducing energy intensity in Inner Mongolia, Xinjiang, and Hebei is the key to solving China's increasing energy intensity “gap” dilemma. The factors causing the energy intensity divergence in Inner Mongolia and Xinjiang are related to lagging economic growth and low energy efficiency, which impedes carbon emission reductions significantly. The factors causing the divergence of energy intensity in Hebei are rooted in its heavy industrial structure. Our findings are directly applicable to crafting regional energy policy with more targeted and practical emission reduction programs.

Research and development factor mismatch, production losses, and energy efficiency: A study on Chinese regional economies

In the face of global economic development and increasing energy demands, the efficient allocation of Research and Development (R&D) resources emerges as a universal concern. This study illuminates the complex relationship between R&D factor mismatch, production losses, and changes in regional energy intensity. Utilizing factor mismatch accounting framework and the STIRPAT model, we systematically assess the impact of spatial mismatches in R&D factors on R&D output and regional energy intensity. The analysis unfolds in three steps, first, the estimation of the degree of spatial R&D factor mismatch and the resulting output gap; second, the calculation of R&D output loss as a function of spatial mismatch coefficient and output share per location; and third, the examination of changes in energy intensity induced by R&D factor mismatch in terms of Total Factor Productivity (TFP), factor mismatch, and output structure. Our findings reveal that R&D factor mismatches can hinder energy intensity reduction, and that regional disparities in the process of factor market marketization significantly contribute to variations in regional energy intensity. These insights emphasize the critical role of efficient R&D resource allocation in promoting energy conservation and productivity, providing substantial implications for policy-making in sustainable growth and energy efficiency across diverse economies.

Development of trends in the energy intensity of regional economies

Aim. To develop recommendations of strategic nature for regional authorities to make decisions aimed at reducing energy intensity and electricity intensity of regional economies, taking into account the identified trends of changes in the process of analyzing the calculated data and applying the typological grouping of the country’s regions.Objectives. To analyze normative legal acts, reporting documents of federal and regional authorities, as well as publications devoted to the research topic; to consider the possibilities of applying to the analysis of energy intensity of regional economies along with the sectoral territorial approach; to present the methodology of analysis of energy intensity of regional economies based on the decomposition of energy intensity and subsequent typological grouping of regions on several grounds; to characterize the obtained results and to determine strategic solutions for the reduction of energy intensity and electricity intensity of regional economies.Methods. Along with general scientific methods of research, such as analysis, generalization, analogy, description, the methods of statistical data processing, including index, methods of average values, dynamic series, balance, aggregation, statistical and typological groupings, summary were applied.Results. Along with the sectoral approach to analyzing the energy intensity of the Russian economy, used primarily by the federal authorities, the territorial approach was considered. The latter makes it possible not only to identify trends and latent factors affecting the energy intensity of the gross regional product (GRP), but also to manage it within the framework of the regional authorities’ targeted policy to reduce energy intensity. By means of the proposed methodology, a typological grouping of regions was carried out, which makes it possible to identify the subjects of the Russian Federation (RF) with the highest values according to the growth rates of electricity intensity and energy intensity, in respect of which it is necessary to use a differentiated approach and incidental measures that take into account the influence of latent factors and the sectoral structure of the regional economy.Conclusions. From the methodological point of view, the article may be of interest to the scientific community focused on finding solutions in the context of improving the energy efficiency of regional economies, and from the practical point of view to regional authorities, whose competence includes the issues of reducing the energy intensity of economies. The study contains elements of novelty, manifested in the methodology of analyzing regional electricity and energy intensity, application of territorial and sectoral approach to improve regional energy efficiency. The article contributes to the evaluation support of management decisions at the regional level in the framework of the state task to reduce energy intensity of the Russian economy.

Effects of regional freight structure and energy intensity on CO2 emission of transport—a case study in Yangtze River Delta

Taking the Yangtze River Delta (YRD) of China as the research area, this paper studies the influence of freight structure adjustment and energy intensity on carbon dioxide (CO2) emission from the transportation industry. Sample data from 1990 to 2019 are selected, and co-integration analysis is performed using three independent variables: energy intensity, turnover ratio of railway to highway (R/H), and turnover ratio of railway to waterway (R/W). Then, an autoregressive distribution lag-error correction model (ARDL-ECM) is established to estimate the long-run and short-run relationships among the variables through unit root test, autoregressive distributed lag (ARDL) boundary test, and Granger test. The results show that in the long run, the growth of energy intensity leads to the long-term growth of CO2 emission in the transportation sector of the YRD, and R/W and R/H have a suppressive effect on CO2 emission. Granger causality indicates that there is a bidirectional causal relationship between energy intensity and CO2 emission. This work can be a reference for government departments to formulate policies related to carbon emissions in the transportation industry.

Investigating the role of enterprises' property rights in China's provincial industrial energy intensity

Continuous decentralisation of the property rights of state-owned enterprises (SOEs) constitutes one of the most important market-oriented reforms in China. However, previous studies have seldom investigated the relationship between changes to enterprises' property rights and energy intensity. As industrial departments consume most of China's total energy, this study examines the influence of changes to enterprises' property ownership on industrial energy intensity across China's provincial regions. Based on a series of econometric analysis and China's 30 provincial regions between 2005 and 2019, the empirical results indicate that decentralising industrial SOEs' property rights is beneficial for controlling industrial energy intensity. This study also found that regions' degree of openness accelerated this effect. In particular, although the expansion of SOEs increases energy intensity, openness helps SOEs enhance energy sustainability. However, there is significant heterogeneity between different regions and periods. Furthermore, we investigate the roles of research and development (R&D) efficiency and structural change that explain how the decentralisation of SOEs' property rights influences regional industrial energy intensity. The results could help policymakers control the industrial energy intensity in the background of market-oriented reforms.

The environmental externality of China’s digital infrastructure: does institution supply matter?

ABSTRACT Whether the digital infrastructure can resolve the conflict between environmental concerns and economic development is an urgent issue to be addressed in China. By taking the implementation of ‘Broadband China’ policy as a quasi-natural experiment and using 15 million policy statements to measure the environmental protection institution (EPI), this study investigates the environmental externality of urban digital infrastructure and the nonlinear supporting role of EPI. It is found that urban digital infrastructure is conducive to reducing regional energy intensity and carbon emission intensity. After a series of robustness tests, the conclusions are still credible. Mechanism analysis shows that promoting the structural upgrading of the service industry, accelerating the intensive development of the manufacturing industry and improving the quality of regional innovation are three impact channels. Furthermore, the EPI plays a nonlinear supporting role in the process of digital infrastructure’s promoting regional energy saving and emission reduction. This study incorporates the supply of environmental protection institution into the analytical framework, thus providing a new perspective for comprehending the externality of digital infrastructure and environmental protection institution.

Has the Development of the Digital Economy Reduced the Regional Energy Intensity—From the Perspective of Factor Market Distortion, Industrial Structure Upgrading and Technological Progress?

The digital economy has become the key driver of improving the quality and efficiency of the social economy and reshaping core competitiveness with the new technological revolution. This paper uses China’s provincial panel data from 2011 to 2019 and the entropy method to measure the digital economy development index of China’s provinces and cities and examines the impact of digital economy development on regional energy intensity using the fixed effect model. The results show that: (1) There is a significant difference in the development levels of the digital economy in the eastern and western regions of China. In addition, there is also a significant difference in the intensities of energy use between the northern and southern regions. (2) The development of the digital economy can significantly reduce China’s regional energy intensity. (3) There are three intermediary transmission paths for digital economy development to reduce the energy intensity through alleviating factor market distortion, promoting industrial structure upgrading and improving the technological innovation level, which are optimizing factor allocation, reducing factor distortion, adjusting and upgrading industrial structure and driving technological innovation applications. In general, all regions should further promote the development of digital industrialization and industrial digitization, enhance digital technology innovation and application, deepen the integrated development of digital economy and real economy and promote a new round of technological change and industrial upgrading on the basis of optimizing the allocation of important factors such as information data and artificial intelligence.

Multi-dimensional Analysis of the Synergistic Effect of Pollution Reduction and Carbon Reduction in Tianjin Based on the STIRPAT Model

Based on the STIRPAT model, this study quantitatively analyzed the synergistic effect of pollution reduction and carbon reduction in Tianjin from three dimensions:total emission, emission reduction, and synergy coefficient. The results showed that the main emission sources of air pollutants and greenhouse gases in Tianjin were industrial sources, and the Pearson correlation coefficient of air pollutants and greenhouse gases was 0.984. The total population, urbanization rate, gross regional product, energy intensity, and carbon dioxide emission intensity were important factors affecting the synergistic effect of pollution reduction and carbon reduction in Tianjin. In 2011 and 2012, Tianjin's air pollutants and greenhouse gas emissions increased synergistically, and the synergistic effect coefficients were 0.18 and 0.17, respectively. From 2013 to 2014 and from 2018 to 2023, the air pollutant emission reduction and greenhouse gas emission increased, the synergistic effect coefficient was less than 0, and the pollution reduction and carbon reduction had no synergistic effect. In 2015-2017 and 2024-2060, air pollutants and greenhouse gas emissions were predicted to be reduced at the same time, with a synergistic effect coefficient ranging from 2.74 to 8.76. Tianjin had the conditions to enter the synergistic stage of pollution reduction and carbon reduction in 2024. The most important things for Tianjin to do to promote the synergy of pollution reduction and carbon reduction were to strictly control the total amount of greenhouse gas emissions, continue to promote the reduction in energy intensity and carbon dioxide emission intensity, and reasonably control the total population, urbanization rate, and regional GDP.

The Impact of Financial Development and Green Finance on Regional Energy Intensity: New Evidence from 30 Chinese Provinces

Energy efficiency and energy intensity are gradually gaining attention, and it is now an important proposition to reconcile financial development, green finance, and regional energy intensity. Using Chinese mainland provincial panel data (except Tibet) from 2007 to 2019, this paper applied the spatial econometric model and the panel threshold model to investigate the effects of financial development and green finance on regional energy intensity. The paper discovered that financial development raises regional energy intensity, while green finance reduces it. Based on the panel threshold perspective, in different stages of green finance development, the effect of financial development on regional energy intensity presents an inverted U-shaped effect that first promotes and then inhibits. Meanwhile, green finance has a significant positive spatial transmission effect on regional energy intensity. Based on the spatial weight matrix reflecting regional economic relations, the increase in energy intensity has a significant negative spatial autoregressive effect on itself, and the spatial spillover effect of financial development is negligible.

What Mechanisms Do Financial Marketization and China's Fiscal Decentralization Have on Regional Energy Intensity? Evidence Based on Spatial Spillover and Panel Threshold Effects Perspectives.

Energy efficiency and energy intensity are gradually gaining attention, and it is now an important proposition to reconcile financial marketization, fiscal decentralization, and regional energy intensity. Using Chinese mainland provincial panel data (except Tibet) from 2007 to 2019, this study applied the dynamic panel system generalized method of moments model, the spatial Durbin model, and the panel threshold model to investigate the mechanisms of financial marketization and fiscal decentralization on regional energy intensity. The study found that financial marketization can play a significant role in suppressing regional energy intensity, while fiscal decentralization promotes energy intensity. Meanwhile, financial marketization in one province can have a negative spatial transmission effect on energy intensity in other provinces, while fiscal decentralization in one province has a negative spatial spillover effect on energy intensity in other provinces. Based on the analysis of the moderating and threshold effects, financial marketization not only moderates the negative externality of fiscal decentralization, making it inhibit energy intensity in the opposite direction, but also gradually increases the moderating effect on fiscal decentralization as the degree of financial marketization increases, showing a nonlinear inhibiting effect on regional energy intensity.

Impact of the “Low-Carbon City Pilot” Policy on Energy Intensity Based on the Empirical Evidence of Chinese Cities

Reducing energy intensity is conducive to the sustainable use of non-renewable fossil energy, and is also one of the main strategies to deal with climate change and environmental degradation. The effect of national macro-level factors on energy intensity has been basically confirmed, but the effect of regional low-carbon policy remains to be investigated. Based on this, our analysis exploits China’s “low-carbon city pilot” policy as a quasi-natural experiment and conducts the difference-in-difference resign. We collect the panel data of 271 cities in China from 2006 to 2016. The empirical results show that: first, the low-carbon city pilot policy can effectively reduce the energy intensity. Second, there exist heterogeneous effects on energy intensity among different cities, and the inhibition effects in eastern cities, high economic development cites, and non-old industrial-base cities are more significant. Third, the policy mainly affects regional energy intensity through technological innovation rather than industrial structure optimization mechanism.

Exploring the spatiotemporal evolution of energy intensity in China by visual technology of the GIS

Fully understanding the spatiotemporal evolution of energy intensity is the key to formulating regional heterogeneous energy policies in China. In this paper, the relative variation of energy intensity among provinces in China from 2003 to 2018 is captured by the Geographic Information System (GIS) to analyze the spatiotemporal evolution of regional energy intensity and the spatiotemporal evolution of spatial correlations between regions. The results show that the energy intensity in eastern, central, western, and northeastern China shows a downward trend. Furthermore, the main geographical distribution area of energy intensity is in the Yellow River Basin.The spatial distribution of energy intensity showed a trend of dispersion with increasing ellipse area from 3.611×106 km2 to 3.861×106 km2 and generally shifted westward, with the weighted average center from (110∘20’E, 35∘9’N) to (108∘42’E, 35∘26’N). The spatial aggregation of energy intensity in China is significant, forming high energy intensity clusters mainly in the western region and low energy intensity clusters mainly in the eastern coastal region. The pattern of the spatial distribution of energy intensity in China shows spatial locking and path dependence. Our findings provide a more comprehensive perspective of the spatiotemporal evolution of energy intensity in China.

The Characteristics and Spatial Spillover Effects of Green Technology Innovation on Regional Energy Intensity

Green technology innovation (GTI) has become a basic force for promoting energy development transformation. China must play the role of the first driving force of GTI to achieve low-carbon sustainable development. Based on the relevant panel data of 30 Mainland Chinese provinces from 2007 to 2017, this paper calculates the GTI level in each province through the Slack Based Model- Global Malmquist-Luenberger (SBM-GML) model. This paper uses the Dynamic Spatial Dobbin Model (DSDM) to analyze the spatial spillover of GTI on regional energy intensity (EI) from the time and space dimensions. This study finds that GTI has achieved positive quarter-on-quarter growth each year, but innovation efficiency is low, resulting in a small overall annual innovation improvement rate. Moreover, the level of GTI in the eastern region is much higher than that in the central and western regions, and regional heterogeneity is gradually increasing. China's regional EI shows significant positive agglomeration characteristics. High EI agglomeration is mainly concentrated in major energy provinces such as those in Northwest and Northeast China, while low EI agglomeration mainly occurs in developed provinces in East and Central China with complete infrastructure and strong technological innovation.

Exploring the spatial aggregation and determinants of energy intensity in guangdong province of China

Guangdong has become the province with the second largest energy consumption and the largest gross domestic product in China. Studying the energy intensity to realise the sustainable development goal of Guangdong is important. Numerous studies on energy intensity hold a common assumption that regions are spatially independent from a static and macro perspective of the country, which may not always be the case. This study focuses on investigating the spatial aggregation and determinants of Guangdong’s energy intensity using the panel data on city level from 2005 to 2016 from a spatial perspective. A novel approach based on the Global Moran-Index (Moran’s I) and Durbin model was proposed to deal with the dilemma. From the analytical results, the findings are as follows: (1) Guangdong’s energy intensity indicates a strong spatial correlation and clear spatial agglomeration characteristics; (2) Economic growth is the most important influential factor of energy intensity in Guangdong, whereas foreign direct investment has the least impact; (3) The spillover effect of technological progress is significant and can effectively reduce regional energy intensity. Moreover, the study provides references for the implementation of enhancing energy utilisation efficiency.

The Roles of Beijing-Tianjin-Hebei Coordinated Development Strategy in Industrial Energy and Related Pollutant Emission Intensities

This study investigates the different impacts of coordinated development in the Beijing–Tianjin–Hebei (BTH) region on industrial energy and pollution intensities based on the difference-in-difference (DID) method and the quantile DID method. The panel data cover industrial energy consumption and three wastes, which are industrial wastewater, sulfur dioxide, and dust emissions, from all 13 cities in the BTH region and 17 cities in Henan Province for the period 2007–2017. The study finds that China’s BTH coordinated development strategy, on average, tends to restrain regional industrial energy intensity, especially in lower quantile level (0.1–0.4) cities. However, it tends to promote industrial energy intensity in higher quantile level (0.7–0.9) cities. The impacts on pollution intensities vary among industrial wastewater, sulfur dioxide, and dust emissions. The results suggest that, in addition to paying attention to dust pollution caused by transportation integration in the BTH region, China should also pay more attention to green relocation of industries from Beijing to Hebei and strengthen coordinated environmental regulation while maintaining corporate interests.

A multi-dimensional analysis on microeconomic factors of China's industrial energy intensity (2000–2017)

Reducing energy intensity is the key point to solve the contradiction among economic development, energy constraints and environmental pressure in China. Considering microeconomic factors, this paper extends decomposition and attribution models, and analyses the drivers of China's industrial aggregate energy intensity (AEI) at the regional/sectoral level. The results show that AEI decreased by 49% during 2000–2017. At the regional level, most provinces presented negative contribution on regional energy intensity effect. The AEI decline was attributed to the drop of greatest negative effect of R&D efficiency in Liaoning. The increase in investment intensity and R&D intensity lead to an increase in AEI, largely owning to Shandong and Liaoning, respectively. At the sectoral level, the AEI decline can be mainly explained by the inhibition effect of energy intensity and R&D efficiency. However, this negative contribution was greatly offset by the R&D intensity effect. The investment intensity showed a positive impact in AEI increase, and smelting and pressing of ferrous metals sector was the main contributor. Regional and sectoral investment structure effect shifted from increasing to decreasing AEI over the study period. Based on the results, targeted policy recommendations are proposed to further decrease the China's industrial AEI in the 14th FYP period.

Assessment of the improvement in energy intensity by the new high-speed railway in Japan

Energy intensity improvement is an influential policy agenda for global environmental problem-solving and sustainability enhancement. This study focuses on inter-regional networks’ impact on energy intensity and statistically examines whether the improvement of such networks promotes a modal shift from passenger vehicles to railways. The results indicate the nonlinear relationship between the improvement of inter-regional networks and the variation of the passenger vehicle sector’s energy consumption. Furthermore, the results reveal that the laying of high-speed railways, which helps strengthen inter-regional networks, can significantly improve regional energy intensity. This result has valuable implications for Japan’s national land plan. The high-quality transportation infrastructure, such as high-speed railways, to be introduced in the country can potentially change the energy consumption patterns in inter-regional travel and improve energy intensity. Our results suggest that the installation of the high-speed railway will effectively reduce carbon dioxide emissions. This revelation is expected to provide new policy evidence to scholars and policymakers.

Analysis of the Influencing Factors of Regional Carbon Emissions in the Chinese Transportation Industry

Global warming caused by excessive emissions of CO2 and other greenhouse gases is one of the greatest challenges for mankind in the 21st century. China is the world’s largest carbon emitter and its transportation industry is one of the fastest growing sectors for carbon emissions. However, China is a vast country with different levels of carbon emissions in the transportation industry. Therefore, it is helpful for the Chinese government to formulate a reasonable policy of regional carbon emissions control by studying the factors influencing the carbon emissions of the Chinese transportation industry at the regional level. Based on data from 1997 to 2017, this paper adopts the logarithmic mean divisia index (LMDI) decomposition method to analyze the influencing degree of several major factors on the carbon emissions of transportation industry in different regions, puts forward some suggestions according to local conditions, and provides references for the carbon reduction of Chinese transportation industry. The results show that (1) in 2017, the total carbon emissions of the Chinese transportation industry were 714.58 million tons, being 5.59 times of those in 1997, with an average annual growth rate of 9.89%. Among them, the carbon emissions on the Eastern Coast were rising linearly and higher than those in other regions. The carbon emissions in the Great Northwest were always lower than those in other regions, with only 38.75 million tons in 2017. (2) Economic output effect is the most important factor to promote the carbon emissions of transportation industry in various regions. Among them, the contribution values of economic output effect to carbon emissions on the Eastern Coast, the Southern Coast and the Great Northwest continued to rise, while the contribution values of economic output effect to carbon emissions in the other five regions decreased in the fourth stage. (3) The population size effect promoted the carbon emissions of the transportation industry in various regions, but the population size effect of the Northeast had a significant inhibitory influence on the carbon emissions in the fourth stage. (4) The regional energy intensity effect in most stages inhibited carbon emissions of the transportation industry. Among them, the energy intensity effects of the North Coast and the Southern Coast in the two stages had obvious inhibitory influences on carbon emissions of the transportation industry, but the contribution values of the energy intensity effect in the Great Northwest and the Northeast were positive in the fourth stage. (5) Except for the Great Southwest, the industry-scale effects of other regions had inhibited the carbon emissions of transportation industry in all regions. (6) The influences of the carbon emissions coefficient effect on carbon emissions in different regions were not significant and their inhibitory effects were relatively small.