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How to improve total factor energy efficiency? An empirical analysis of the Yangtze River Economic Belt of China
Abstract
This study employs a Luenberger-Hicks-Moorsteen productivity indicator to measure the total factor energy efficiency (TFEE) of the Yangtze River Economic Belt (YREB) of China from 2005 to 2016. The relative importance analysis method is used to investigate the driving forces of the TFEE. The main results are as follows. (a) The TFEE has an average growth rate of 3.3% during the study period. The TFEE declines first, then rebounds, and declines again. The contribution of technological efficiency changes to the TFEE growth is almost zero which indicating no catch-up effect in the sample period. Technological progress contributes a negative impact to the TFEE, with an average annual decrease rate of 3.4%. The scale efficiency changes, with an average annual growth rate of 6.7%, are extremely important for the growth of TFEE. (b) The TFEE of the western region is narrowing the gap with the central and western regions, while the gap between the central region and the eastern region is increasing. (c) Research investment can improve the growth of TFEE significantly, while government expenditure and industrial structure are not. And government expenditure, economic development, and research investment are top factors to explain the variation of TFEE.
... Compared with the industrial sector, the energy utilization efficiency of the agricultural sector is usually low, and the energy consumption of agricultural activities has an important impact on global environmental conditions and climate conditions [7][8][9]. In comparison with other countries, China's energy inputs and outputs also lag far behind those of developed countries and even some developing countries, which is essentially due to low energy efficiency [10]. With the further advancement of China's agricultural mechanization and modernization, China's agricultural energy consumption will continue to maintain an increasing trend in the future [11]. ...
... The group-frontier agricultural total-factor energy efficiency (GATFEE it ) can be expressed by Equation (10), which is the reciprocal of the first term on the right side of Equation (9) of province i in period t. ...
... Based on the above analysis, we used the translog stochastic frontier cost function model to estimate the cost function parameter (β (j) ) of the high-energy-input group, the medium-energy-input group, and the low-energy-input group and calculate the GATFEE it of each province using Formula (10). For the meta-frontier cost function model, we used quadratic programming with equality constraints to solve the parameter value, β * , as shown in Equation (15). ...
Improving agricultural energy efficiency is essential in reducing energy consumption and achieving agricultural sustainable development. This paper aims to measure the agricultural total-factor energy efficiency in China rather than the partial-factor energy efficiency while taking full account of regional heterogeneity and to investigate the driving factors of agricultural total-factor energy efficiency. The empirical results showed that the average value of agricultural total-factor energy efficiency is 0.814 in China, and the technological gap ratio is 0.853. The regional difference in agricultural total-factor energy efficiency was quite obvious. Higher agricultural energy inputs are associated with higher agricultural total-factor productivity. The total value of potential agricultural energy savings in 30 provinces of China reached 1704.41 billion tons of standard coal. In terms of the absolute amount of agricultural energy saving, the amount was largest in the low-energy-input area, which was 113.87 million tons of standard coal, accounting for 66.81% of the total potential saving amount. Furthermore, we used the Tobit model to analyze the influencing factors of agricultural total-factor energy efficiency. We found that the proportion of agriculture to GDP has a positive impact on agricultural total-factor energy efficiency, while the per capita income of farmers, fiscal support for agriculture, the illiteracy rate of farmers, agricultural labor input, and agricultural capital stock have a negative impact on agricultural total-factor energy efficiency. Finally, we proposed policy implications in terms of agricultural technological progress, agricultural infrastructure, technical training, etc.
... As an essential input factor of production and life, energy is a significant driving force for economic development (Tang and He, 2021) [1]. However, energy promotes economic growth, and brings severe environmental problems. ...
... As an essential input factor of production and life, energy is a significant driving force for economic development (Tang and He, 2021) [1]. However, energy promotes economic growth, and brings severe environmental problems. ...
... Then, we conducted a benchmark regression analysis based on panel data from 275 cities in China from 2011 to 2019. Moreover, to minimize the estimation deviation caused by omitting other variables, six control variables were added to the benchmark model to build the model (1) (refer to Tang and He (2021) [1], Yu (2018) [36], Wang and Cao (2019) [37]): the agglomeration of productive services ( ), human capital ( ), expenditure on science and technology ( ), foreign direct investment ( ), foreign trade ( ), and infrastructure ( ). We treated these as the control variables and logarithmically processed the non-proportionate variables. ...
The widespread application of new-generation information technology, such as big data and artificial intelligence, has promoted the development of economic and technological transformation and the deep integration of digital and real economies. The digital economy is an essential force of China in the new era and it is promoting China’s economic development in a high-quality way. In this study, we theoretically describe the mechanism of the digital economy that affects total-factor energy efficiency and empirically analyze the impact of digital economy development on total-factor energy efficiency using data from 275 cities at the prefecture level and above in China from 2011 to 2019. We found that the digital economy has significantly improved total-factor energy efficiency. We used instrumental variable estimation and the replacement of explanatory variables to test the robustness of our results, finding that our conclusions were valid. Technological innovation, industrial structure optimization, and resource misallocation improvement are the channels through which the digital economy affects total-factor energy efficiency. Resource misallocation at the city level as the intermediary variable was this paper’s research gap. Further research showed that the improvement effect of the total-factor energy efficiency in eastern regions and megacities was more evident under the influence of the digital economy. All regions in China should combine their resource endowments to further release the dividends of the digital economy, enabling it to best promote total-factor energy efficiency. The relevant departments of the government should also stimulate market demand and promote the deep integration and balanced development of the digital economy and energy industry in low-energy-efficiency cities.
... This suggests that IFDI and OFDI have been concentrated in the eastern region but have had almost no distribution in the western region, thus leading to unbalanced development. In addition, many scholars have found significant differences in the GEE among these regions (Xia and Xu, 2020;Yuan et al., 2020;Tang and He, 2021;Chen and Wang, 2022). Therefore, both GEE and bilateral FDI may have spatial agglomerations. ...
... Most of the current research on energy efficiency considers desired output from an economic point of view and does not consider undesired output such as environmental pollution, so the measurement results overestimate energy efficiency (Wu et al., 2020). Some scholars have introduced the undesirable output from energy consumption into the efficiency measurement called green energy efficiency or total factor energy efficiency (Tang and He, 2021;Meng and Qu, 2022). This paper defines green energy efficiency according to the output method, which refers to maximizing the output achievable after comprehensively considering energy input and environmental damage in production activities. ...
... Some studies recommend that the undesired energy consumption output be considered when calculating energy efficiency to avoid deviation (Chang, 2013;Song et al., 2016;Xiao and You, 2021). This method is also the mainstream academic research framework for measuring energy efficiency (Chang, 2013;Tang and He, 2021). As an improvement over SBM, Super-SBM incorporates the undesired output into the efficiency measure, performing a secondary evaluation of the DEA effective DMU. ...
At present, green energy transformation and climate policy are increasingly connected. The need to improve national competitiveness and implement climate and energy strategies means that an energy-consuming country like China must rapidly enhance its green energy efficiency (GEE) and energy consumption structure. The following paper contributes to the existing literature by analyzing the effects of bilateral FDI on GEE from the perspective of spatial correlation. Based on data from 30 Chinese provinces between 2003 and 2020, this paper uses multiple undesired output indicators covering eight pollutants to measure GEE and then uses the coupling function to measure the coordinated development level of bilateral FDI (CDFDI). According to the Panel Vector Autoregression model, there is a coupling relationship between bilateral FDI. Both the global Moran index and the local Moran scatter plot shows that GEE and CDFDI are characterized by spatial aggregation. This paper adopts the dynamic Spatial Dubin Model for empirical analysis. Our results reveal a significant positive impact of CDFDI on GEE in local and nearby regions. The impact of CDFDI on GEE is moderated by technological diffusion, nationalization, and environmental regulation, resulting in differential impacts on GEE in local and nearby regions. Furthermore, CDFDI with different investment motives has a boosting effect on local GEE. Among them, the mutual investment portfolio between China and developed countries has a negative impact on the local GEE. By contrast, the complementary investment motivation portfolio can boost GEE in local and adjoining regions in any period. This is the first paper to study the spatial effects of green energy efficiency and the heterogeneity of investment incentives to help Chinese international investment policymakers better understand the contribution of CDFDI to improving GEE and inform supportive policies. To improve green energy efficiency, the government must strengthen the implementation of the opening-up policy and create international capital flows suitable for local needs.
... Efficiency has become the main focus of green development, but it is defined by various names, even when similar measurements are used. The modified total-factor framework from neoclassical economic growth theory is used to define efficiency, resulting in different terms, such as environmental efficiency (Twum et al., 2021;Wang et al., 2022b;Salman et al., 2022), green economic efficiency (GEE) (Zhao et al., , 2022Zhuo and Deng, 2020;Liu et al., 2022;Wang et al., 2022a;Wang et al., 2022c), total-factor efficiency (Zhang et al., 2021), total-factor energy efficiency Ohene-Asare et al., 2020;Tang and He, 2021), and total-factor carbon emission efficiency Lv et al., 2021). These different names highlight the heterogeneity of selected nondesired outputs. ...
... The multiple-factor indicators measuring the efficiency of green development are defined by various names within a similar total-factor framework. These include energy efficiency (Barberio Mariano et al., 2016), eco-efficiency , circular economy performance (Wang et al., 2021), environmental efficiency (Twum et al., 2021;Wang et al., 2022b;Salman et al., 2022), GEE (Zhao et al., , 2022Zhuo and Deng, 2020;Liu et al., 2022;Wang et al., 2022a;Wang et al., 2022c), total-factor efficiency (Zhang et al., 2021), total-factor energy efficiency Ohene-Asare et al., 2020;Tang and He, 2021), and total-factor carbon emission efficiency Lv et al., 2021). While these names reflect the heterogeneity of selected nondesired outputs, they are based on similar measurements and a modified input-output framework originating from neoclassical economic growth theory that includes energy and non-expected outputs. ...
Carbon lock-in is a major obstacle to transforming carbon-based energy systems toward carbon peaking and neutralization, affecting the green economy. However, its impacts and paths on green development are unclear, and it is difficult to represent carbon lock-in using a single indicator. This study measures five types of carbon lock-ins and their comprehensive effect using the entropy index of 22 indirect indicators in 31 Chinese provinces during 1995-2021. Moreover, green economic efficiencies are measured using a fuzzy slacks-based model considering undesirable outputs. The panel Tobit models are used to test the impacts of carbon lock-ins on green economic efficiencies and their decompositions. Our results show that provincial carbon lock-ins in China range from 0.20 to 0.80, with notable type and regional differences. Overall carbon lock-in levels are similar, but the severity of different carbon lock-in types varies, with social behavior being the most serious. However, the overall trend of carbon lock-ins is declining. Low pure green economic efficiencies, rather than scale efficiencies, contribute to China's worrisome green economic efficiencies, but they are decreasing and accompanied by regional gaps. Carbon lock-in hinders green development, but a specific analysis is needed for different carbon lock-in types and development phases. It is biased to assume that all carbon lock-ins hinder sustainable development, as some are even necessary. The impacts of carbon lock-in on green economic efficiency depend more on its effect on technology than on scale change. Implementing various measures to unlock carbon and maintaining reasonable levels of carbon lock-in can promote high-quality development. This paper may promote the development of new unlocking CLI measures and sustainable development policies.
... As of 2020, China's energy consumption has reached 4.98 billion tons of standard coal. Energy consumption not only produces a large amount of environmental pollutants, resulting in a decline in environmental quality, but also has adverse effects on sustainable economic development and human health (Fatemeh et al., 2019;Tang & He, 2021). As China's coal resources have obvious reserves and price advantages, the proportion of coal consumption in the energy consumption structure remains above 60%, which means that the coal-dominated energy consumption structure will not change in the short term (Yu & Shen, 2020). ...
... Cornillie & Fankhauser (2004) studied the energy efficiency of various countries and found that the energy efficiency of most transition countries is higher than that of developed countries. Tang & He (2021) measured the energy efficiency of China's Yangtze River Economic Belt from 2005 to 2016, and found that the energy efficiency gap between the western region and the central and western regions is narrowing, while the gap between the energy efficiency of the central region and the eastern region is widening. Chen & Lin (2021) employed a data-driven clustering approach and convergence analysis and found that energy efficiency gaps persist across clubs. ...
This paper uses the exogenous shock of China’s formal implementation of the environmental information disclosure system in 2008 to construct a quasi-natural experiment. Based on the panel data of prefecture-level cities from 2003 to 2019, the propensity score matching and difference in difference (PSM-DID) approach was used to systematically evaluate the impact of environmental information disclosure on energy efficiency. Overcoming the difficulties in measuring environmental information disclosure and the endogenous problem, this paper investigates the energy-saving effect of environmental information disclosure and illustrates its mechanism for the first time. The results show that environmental information disclosure significantly improved energy efficiency, and public participation played an important role in energy conservation, a conclusion that remained true after a series of robustness tests. The test of the impact mechanism shows that environmental information disclosure can achieve the goal of improving energy efficiency by promoting industrial structure upgrading and technological innovation. This paper enriches the discussion on the relationship between environmental information disclosure and energy efficiency, and provides useful policy inspiration for improving the level of energy efficiency and achieving sustainable economic development.
... Environmental regulatory policies can strengthen total-factor energy efficiency, exhibiting a "U"-shaped relationship with it (Wu et al., 2020a;Galeotti et al., 2020). Furthermore, economic development and technological advancements can also improve energy efficiency (Tang and He, 2021). ...
The advancement of Financial Technology (FinTech) is crucial for government entities, the National Grid, and various energy corporations to facilitate the transition towards sustainable and green production methods. This study investigates the relationship between FinTech and Total Factor Energy Efficiency (TFEE) using data from a selected sample of 254 city groups in China. We examine how the development of FinTech impacts TFEE from both non-spatial and spatial perspectives. The results from the non-spatial panel model indicate that FinTech development has a significant positive impact on TFEE. Comparative studies were conducted using fixed effects (FE), feasible generalized least squares (FGLS) models, and system generalized method of moments (GMM) models, and the main findings remained consistent, confirming the robustness of our conclusions. Spatial autocorrelation results reveal a significant positive spatial spillover effect on TFEE. Both the spatial Durbin model and the dynamic spatial Durbin model demonstrate that FinTech also has a significant positive impact on TFEE, and this effect increases over time. These conclusions remain robust even after considering various spatial weight matrices and alternative methods for calculating TFEE. Additionally, we discovered that the digital economy plays a vital role in strengthening the relationship between FinTech and TFEE. Heterogeneity analysis indicates that, compared to cities without resource-based economies, FinTech development in growing resource-based cities has a more substantial impact on TFEE.
... The government intervenes in social development mainly through economic intervention and policy orientation, in which fiscal expenditure is the primary method of economic intervention. Researchers have studied the relationship between factor productivity and fiscal expenditure, finding that the emission of sulphur dioxide and other pollutants reduces with an increase in public financial expenditure 13 , which can significantly promote total factor energy efficiency 14 . Zhang et al. 15 found that government fiscal expenditure can improve EE; however, many researchers also concluded that fiscal expenditure will negatively affect factor productivity. ...
This study investigates the relationships among government intervention, industrial structure, and energy eco-efficiency (EE). Energy eco-efficiency was measured based on a non-radial directional distance function for 236 cities in China from 2005 to 2019. Additionally, the difference-in-difference model (DID) method and spatial econometric models were used to analyse the impact of government intervention and industrial structure on energy eco-efficiency and their spatial spill-over effects. Government intervention includes fiscal expenditures and policy orientation for new energy demonstration construction. Our results indicate that: China’s EE has a fluctuating upward trend and increased 17.85% in the period, and its spatial distribution imbalance gradually developed into a regional distribution balance. Moreover, government intervention and adjustment of the industrial structure improved urban energy eco-efficiency by 7.43% and 0.92%, respectively, which also has spatial spill-over effects in neighbouring regions. Furthermore, economic development, technological innovation, and foreign direct investment enable EE. However, urbanisation hinders the improvement of energy eco-efficiency. Finally, heterogeneity analysis showed that the policy of the new energy demonstration city has better effects on eastern and western cities in promoting EE.
... This decomposition is in line with Fox (2014, 2017) and Ang and Kerstens (2017). The LHM TFP indicator with this decomposition of Fox (2014, 2017) and Ang and Kerstens (2017) is also applied in the studies by Hamid and Wang (2022) and Tang and He (2021), among others. ...
The measurement of economic growth is important for identifying the development patterns followed by different economies. In the light of sustainable development goals, one needs to be able to track the green growth, i.e., they must the adjusted in regard to generation of undesirable outputs that are usually non-marketed. This contribution puts forward an empirical case of the economically developed countries grouped in OECD and measures their total factor productivity (TFP) growth. This is done by exploiting a novel formulation of the Luenberger–Hicks–Moorsteen (LHM) TFP indicator based on the Kuosmanen (Am J Agric Econ 87(4):1077–1082, 2005) proposal. We argue that undesirable outputs must be regarded as special outputs but not inputs in both the production technology and TFP measure. We compare two models: one that considers undesirable outputs as special outputs in the directional distance functions of TFP indicator following Kuosmanen (Am J Agric Econ 87(4):1077–1082, 2005), and another that considers undesirable outputs as inputs following Abad (J Environ Manage 161:325–334, 2015). This proposed approach assumes that input- and output-orientations are taken, with the latter handling both desirable and undesirable outputs simultaneously. Still, we compare our results with those based on the other more conventional frameworks. The empirical case deals with OECD country-level data for 1991–2019. The results suggest that there exist substantial differences in the resulting measures of the TFP growth depending on the distance functions used in the calculation of the LHM indicator.
... How to improve energy efficiency, especially GTFEE at province, cities and firm level, is currently a hot topic of great interest to academia, and there are extensive works in the literature on what factors affect and how to influence GTFEE (Yang et al., 2022). Some literatures calculated and evaluated how heterogeneity policies and factors affect GTFEE from the micro-firm level (Haider et al., 2019;Bu et al., 2022), macro-province and regional level (Tang and He, 2021). In relation to GTFEE of cities, many scholars in China and elsewhere have explored the factors influencing energy efficiency from various aspects. ...
The implementation of the new ambient air quality standards (NAAQS) in 2012 is a milestone in China’s environmental information disclosure process. The fully automated collection and publication of pollution information provides a convenient way to measure the environmental protection process around the country. This paper investigates the association between NAAQS and green total factor energy efficiency (GTFEE) enhancement in Chinese resource-based cities, using the generalized multiperiod DID method and 2004–2019 panel data from 282 prefecture-level cities. We find that the implementation of this NAAQS significantly promotes GTFEE’s improvement in China’s resource-based cities. Furthermore, we discover that both the type of industrial base and the initial public monitoring motivation influence the improvement effect of the NAAQS on GTFEE. In further studies, NAAQS enhances GTFEE through industrial structure optimization, and the magnitude of the local NAAQS effect is influenced by the level of green innovation. Finally, we make recommendations including implementing targeted environmental regulations and enhancing environmental information regulation.
... Many EIRs in China, such as the Beijing-Tianjin-Hebei metropolitan area, the Yangtze River Economic Belt (YREB), and the Guangdong-Hong Kong-Macao Greater Bay Area, have already generated significant economic achievements [25][26][27][28][29][30]. Among them, the YREB performs the best [31,32], but there are also potential risks of ECEs-IPT that need to be identified and regulated. The YREB is relatively scarce in coal resources [33], with only Guizhou, Anhui, Yunnan, and Chongqing being rich [34][35][36]. ...
On account of the long-term dependence on energy trade and the phenomenon of embodied carbon emissions in interprovincial trade (ECEs-IPT), energy supply bases (ESBs) in the economic integration regions (EIRs) are under unprecedented dual pressure of achieving carbon emissions (CEs) reduction targets and ensuring security and stability of the energy supply. This problem has attracted more and more attention and research by experts and scholars. This paper took Anhui, the coal ESB of the Yangtze River Economic Belt (YREB), as an example and took the key stage of rapid development of regional economic integration (REI) and accelerated the realization of CEs reduction targets in YREB from 2007 to 2017 as the study period. From the perspectives of regions and industry sectors, we calculated the transfer amount of ECEs-IPT in Anhui among the YREB, analyzed the spatial–temporal evolution pattern of ECEs-IPT, and revealed the industrial characteristics of ECEs-IPT. Then, we classified the industry sectors and proposed the direction of industrial improvement measures. The results showed that, during the decade, the amount of provinces undertaking the net ECEs-IPT outflow from Anhui increased significantly and spatially expanded from only Jiangxi Province to almost all of the YREB. In addition, 39.77% of the net ECEs-IPT outflow of Anhui was concentrated in petroleum processing, coking, and nuclear fuel processing (RefPetraol), metal smelting and rolling processing (MetalSmelt), and electricity and heat production and supply (ElectpowerProd) that trade with Shanghai, Jiangsu, Zhejiang, and Jiangxi. The analytical model and results will provide a useful reference for the global similar coal ESBs, especially the coal ESBs within the EIRs, to formulate improvement measures for regions or even the world to ensure stability of the energy supply and achieve regional CEs reduction targets.
... As a critical approach to energy conservation and emission reduction, a large number of scholars have been concerned about how to improve industrial energy efficiency, especially industrial energy efficiency. An undisputed fact is that the improvement of the absolute level of various elements can significantly enhance the industrial energy efficiency, e.g., city scale, investment, government intervention, human capital, and transportation, as shown in Table 1 [6][7][8][9][10]31]. Countries around the world are trying to improve energy efficiency through such approaches, thereby alleviating climate deterioration. ...
Previous literatures have focused on the impact of sectorial urban specialization on energy efficiency. As a future trend of urban specialization, the importance of functional urban specialization (FUS) in improving industrial energy efficiency is ignored. To fill this gap, this study explores the impact of FUS on industrial energy efficiency. Utilizing prefecture-level city data from 2005 to 2017, this paper adopts an extended stochastic frontier analysis (SFA) model to measure industrial energy efficiency. Hierarchical linear model (HLM) is used for the estimation of the nested data with cluster-level and city-level, which reduces the bias by nested estimation. The results show that 1) city-level industrial energy efficiency has increased from 0.4133 in 2005 to 0.4461 in 2017, mainly driven by peripheral cities rather than core cities; 2) FUS has a significantly positive impact on industrial energy efficiency in full sample and peripheral cities; 3) FUS reduces the negative effects of city scale and the positive effects of investment and enhances the positive effects of transportation. In addition, the positive effects of FUS on industrial energy efficiency is significant only at low levels of FUS. Practicable policies to improve industrial energy efficiency in China are suggested and applicable to other emerging economies.
... The input side includes production factors such as labor, capital and energy consumption. The desired output is measured by GDP, and considering undesired outputs such as environmental pollution to measure energy efficiency (Shang et al., 2020;Tang and He, 2021). Referring to existing studies (Xiong and Shi, 2021;Zheng, 2021), this paper constructs a total factor energy efficiency index system that emphasizes sustainable development, as shown in Table 1. ...
Based on panel data of 114 prefecture-level resource-based cities in China from 2004 to 2018, this paper uses SBM model and super-efficiency SBM model to measure the energy efficiency of resource-based cities in China, and systematically analyzes the influence of Civilized City Policy on energy efficiency and the mechanism for that influence using difference-indifferences (DID) model and propensity score matching method. The findings show that the spatial distribution of energy efficiency of resource-based cities is higher in eastern China and lower in western China, and the energy efficiency was continually improved, with fluctuations, between 2004 and 2018. DID analysis and robustness analysis prove that Civilized City Policy is significantly conducive to improving energy efficiency of resource-based cities in China. This positive influence can also be achieved through the mechanism of technological innovation. In addition, there exists regional and type heterogeneity in the influence of Civilized City Policy on energy efficiency of resource-based cities.
... ③ Energy input: In addition to the input of production factors such as labor and capital, the production process of enterprises is also inseparable from energy input. Referencing to Borozan (2018), Yang and Wei (2019), Wu et al. (2020), Chen et al. (2021), and Tang and He (2021), this paper selects the total energy consumption in the unit of 10,000 TCE to denote the energy input. ...
The double-wheel driven of manufacturing and producer services industrial co-agglomeration is of great significance for transforming the economic growth mode driven by a single industry, integrating and extending regional resources, and improving energy efficiency. Based on panel data from 2004 to 2019, this paper uses the spatial Dubin model to analyze the impact of industrial co-agglomeration on total factor energy efficiency (TFEE) and its regional heterogeneity. Moreover, the mediating model is employed to examine the mediating effect of green technological innovation in the industrial co-agglomeration affects TFEE. Last but not least, the threshold panel regression model is conducted to verify the nonlinear relationship between industrial co-agglomeration and TFEE. The results show that there is a U-shaped curve relationship between industrial co-agglomeration and TFEE. Moreover, there are obvious regional heterogeneities in the impact of industrial co-agglomeration on TFEE and its spatial spillover effect. Meanwhile, industrial co-agglomeration has a significant indirect impact on TFEE through green technological innovation. In addition, there is a single threshold effect on the impact of industrial co-agglomeration on TFEE, only when the industrial co-agglomeration degree crosses the threshold value of 0.6329, can it positively promote the improvement of TFEE.
... For example, Kantakumar et al. applied RI analysis to study the driving factors of urban growth in Pune [33]. Tang and He used RI analysis to explore the influence factors of total energy efficiency of the Yangtze River Economic Belt based on a panel model [34]. This paper chose the RI method of Ye et al. to discuss the main driving factors of CO 2 emissions in the power industry [35]. ...
The low-carbon transformation of the power industry is of great significance to realize the carbon peak in advance. However, almost a third of China’s CO2 emissions came from the power sector in 2019. This paper aimed to identify the key drivers of CO2 emissions in China’s power industry with the consideration of spatial autocorrelation. The spatial Durbin model and relative importance analysis were combined based on Chinese provincial data from 2003 to 2019. This combination demonstrated that GDP, the power supply structure and energy intensity are the key drivers of CO2 emissions in China’s power industry. The self-supply ratio of electricity and the spatial spillover effect have a slight effect on increasing CO2 emissions. The energy demand structure and CO2 emission intensity of thermal power have a positive effect, although it is the lowest. Second, the positive impact of GDP on CO2 emissions is decreasing, but that of the power supply structure and energy intensity is increasing. Third, the energy demand of the industrial and residential sectors has a greater impact on CO2 emissions than that of construction and transportation. For achieving the CO2 emission peak in advance, governments should give priority to developing renewable power and regional electricity trade rather than upgrading thermal power generation. They should also focus on promoting energy-saving technology, especially tapping the energy-saving potential of the industry and resident sectors.
... The input side includes production factors such as labor, capital and energy consumption. The desired output is measured by GDP, and considering undesired outputs such as environmental pollution to measure energy efficiency (Shang et al., 2020;Tang and He, 2021). Referring to existing studies (Xiong and Shi, 2021;Zheng, 2021), this paper constructs a total factor energy efficiency index system that emphasizes sustainable development, as shown in Table 1. ...
... It can decompose changes in total factor productivity into technological progress and changes in technical efficiency, thereby facilitating in-depth analysis of the causes of changes in total factor productivity. It has been widely used by scholars in energy efficiency research [62]. In this light and, referring to the research of Wang et al. [59], we assume that the form of the production function of the firm is in the form of the Cobb-Douglas production function, and its natural logarithm can be converted to a linear form: ...
Improving energy efficiency is an important way to achieve low-carbon economic development, a common goal of most nations. Based on the comprehensive survey data of enterprises above a designated size in Guangdong Province, this paper studies the impact of artificial intelligence on the energy efficiency of manufacturing enterprises. The results show that: (1) artificial intelligence, as measured by the use of industrial robots, has significantly improved the energy efficiency of manufacturing enterprises. This conclusion is still robust after introducing data on industrial robots in the United States over the same time period as the instrumental variable for the endogeneity test. (2) The mechanism test shows that artificial intelligence mainly promotes the improvement in energy efficiency by promoting technological progress; the impact of artificial intelligence on the technological efficiency of enterprises is not significant. (3) Heterogeneity analysis shows that the age of the manufacturing enterprises inhibits a promoting effect of artificial intelligence on energy efficiency; manufacturing enterprises’ performance can enhance the promoting effect of artificial intelligence on energy efficiency, but this promoting effect can only be shown when the enterprise performance is positive. The paper clarifies both the impact of artificial intelligence on the energy efficiency of manufacturing enterprises and its mechanism of action; this will help provide a reference for future decision-making designed to improve manufacturing enterprises’ energy efficiency.
... In the current research on TFEE, the most commonly selected variables are capital, labor, and energy as input variables and value added as output variable [50,53,100]. Some scholars also chose the value of output as an output variable [101]. ...
The importance and urgency of improving energy and carbon emissions efficiency in mitigating climate change and achieving carbon neutrality have become an increasingly relentless focus in recent years. Assessing the performance of energy saving and carbon emissions reduction is a significant necessity to achieve sustainable economic development. Therefore, from the perspective of production economics, this paper presents a review of the definition, models, and input-output variables for measuring total-factor energy efficiency and total-factor carbon emissions efficiency. Relevant literature in this field, published between 2006 and 2021, has been systematically analyzed using CiteSpace software, which includes a quantitative and visual review of a large body of published literature. This review found that the current definitions of total-factor energy efficiency and total-factor carbon emissions efficiency are confusing and misleading. Furthermore, future research on energy saving and carbon emissions reduction should incorporate subject areas such as economics, energy, and ecology.
... Technological progress and infrastructure [40] and the scale of enterprises, industrial structure, openness, and mechanization level [37] have been proven to have a crucial role in facilitating the IOE-WEF of China. In the influencing factors analysis of single resource efficiency, Tang and He [41] studied the total factor energy efficiency in the YREB and found that government expenditure, economic development level, and R&D input were the main factors affecting energy efficiency. In addition, economic development level and agricultural science and technology input were also the main factors promoting the improvement of cultivated land resource efficiency in the YREB [42]. ...
The high-quality development of the Yangtze River Economic Belt (YREB) plays a crucial role in economic transformation in China. Climate change, rapid population growth, and increased urbanization have contributed towards increased pressures on the water, energy, food (WEF) nexus system of YREB. Thus, there is an imperative need to improve the efficiency of WEF in YREB. However, few studies have conducted spatial-temporal heterogeneity exploration of YREB about the input-output efficiency of WEF (IOE-WEF). Using panel data from 2008–2017, a super slack based model (SSBM), combined with the spatial autocorrelation and spatial econometric method, were proposed to calculate the IOE-WEF of YREB’s 11 provinces, the results indicated that: (1) From the perspective of time, the IOE-WEF in YREB was relatively low and displayed a fluctuating downward pattern while considering the undesirable outputs. (2) From the perspective of space, the spatial distribution of IOE-WEF in YREB was uneven. The efficiency values of the three sub-regions of YREB were “the lower reaches > the middle reaches > the upper reaches”. The IOE-WEF of YREB had a prominent positive spatial correlation and also had a spatial spillover effect. (3) The spatial aggregation effect of IOE-WEF of YREB is gradually weakening. The spatial aggregation types of IOE-WEF in YREB were “high-high” cluster areas in lower reaches and “low-low” cluster areas in upper reaches. (4) From the perspective of driving forces, environmental regulation and technological innovation promoted the improvement of IOE-WEF of YREB, while the industrial structure and mechanization level inhibited the improvement of IOE-WEF of YREB. Furthermore, the role of government support of IOE-WEF of YREB was not obvious. The improvement of IOE-WEF in adjacent regions also had a notable positive spatial spillover effect on the region.
... On the other hand, the countries at higher levels of economic development tend to have higher economic development quality, with more advocating of efficient and energy-saving economic development methods. Compared with competitors in other countries, trading companies in the countries with higher levels of economic development will drive energy-saving, high-efficiency (Tang and He, 2021), and cleanliness in the upstream and downstream of the industrial chain, and will have a stronger motivation for technology research and development in order to occupy a more competitive market globally (Zhao and Lin, 2020). They will accelerate the improvement of the countries' overall energy efficiency and promote the speed of its convergence to a steady state of energy efficiency. ...
It is of great practical significance to explore the impact of trade on energy efficiency convergence for improving the overall energy efficiency, narrowing the energy efficiency gap and promoting the construction of a green Belt and Road Initiative (BRI). Given the large difference in economic development and resource endowment among BRI countries, this paper empirically explores the impact of import and export trade of BRI countries on the convergence of total-factor energy efficiency (TFEE), and the threshold effect with different level of economic development and industrial structure, based on the panel data of 60 BRI countries and the panel threshold model. The results indicate that the import and export trades of BRI countries have not significantly accelerated the convergence process of their overall TFEE. However, the trade of BRI countries exerts a non-linear threshold effect on the TFEE convergence. Specifically, the trade of BRI countries with GDP per capita level over 48436 USD is conducive to accelerating the TFEE convergence, but this impact is not significant in those countries with lower levels of economic development. The trade accelerates the TFEE convergence of BRI countries with industrial share over 71.74%, while the impact is not significant in those countries with relatively lower industrial shares.
Cities are widely recognized as risky environments for humans. Risks to health and well-being are measured in three groups of indices: physiological, emotional and cognitive. Environmental factors discussed in studies include physical factors such as noise, heat load and air pollution, and social factors such as feelings of discomfort, crime, transportation and access to health services. Some studies even argue that stress and health risks are intrinsic to urban environments and that restoration is intrinsic to green environments. This Topic aims to discuss what spaces can be greened and the effect this has on urban environments. It also discusses the effect of the size and layout of parks, vegetation on the walls and roofs of houses, and the effects of different types of vegetation, building materials, and energy efficient design.
Recent events have challenged the way the built environment is designed, managed and experienced. Droughts, wildfires and a global pandemic have undoubtedly forced the building industry to find ways to cope with unpredictable events and to respond to a broader and more complex set of requirements. Today's environment demands new design processes, construction techniques, occupancy practices, and management strategies to increase the resilience of the built environment to extreme, uncontrollable, and unpredictable events while providing healthy and sustainable environments for people. This Topic invites researchers that address this topic to reflect on what the new concept of sustainability for the built environment should be, and to guide new research directions.
The aim of this Topic is to add knowledge to the information on the greening of our cities. We invite the authors to raise awareness of different aspects of the problem or to contribute relevant knowledge on any of the aspects mentioned above.
Global warming is one of the greatest challenges of the contemporary world. Overcoming this problem requires a global determination in energy management and a significant reduction in the use of fossil fuels worldwide. The objectives of this article are to investigate the role of education in economic growth and to evaluate the total-factor energy, economic and environmental efficiency (E3) of oil-producing countries and productivity changes between 2000 and 2019. These countries were divided into two categories of middle- and high-income ones. The panel data model was used to estimate the role of education in the countries' economic growth, and the data envelopment analysis (DEA) method was used to measure their total-factor efficiency (E3). The findings indicate that education has a positive role in economic growth. In general, Norway proved to be efficient in all indicators of e1, e2, e3, and E3. The worst performance in e1 belonged to Canada (0.45) and Saudi Arabia (0.45); in e2, to Algeria (0.67) and Saudi Arabia (0.73); in e3, to the USA (0.04) and Canada (0.08); and in E3, to Canada (0.46), Saudi Arabia (0.48), and the USA (0.64). The average total-factor efficiency of all indicators for the selected countries was low. Average changes in total-factor productivity and technological changes for all the selected countries had decreased in e1 and e3 but improved in e2 and E3 during the studied period. The changes in technical efficiency also decreased during the period. Moving towards a low-carbon economy, developing creative and environment-friendly technologies, more investment in clean and renewable energy, and creating diversity in production, especially for OPEC and countries with single-product economies, are some of the ways suggested for improving E3 efficiency in the countries.
Income inequality refers to the uneven distribution of wealth among population, amongst the discussion it has generated, the issue of equality-based efficiency has become a development goal pursued by many countries. In order to better understand the relationship between income inequality and energy efficiency, the static threshold effect approach and the dynamic threshold effect approach were applied in this article based on panel data of 78 countries. In these approaches, income inequality is the explanatory variable, urbanization and industrialization are the threshold variables, while energy efficiency is the explained variable. The results show that there is a positive coefficient between income inequality and energy efficiency, and the coefficient of this positive relationship decreases when urbanization level rises above the two thresholds 42.68% and 93.16% respectively, and so does it when industrialization level rises above 12.76% and 19.12%. In addition, urbanization plays a greater role than industrialization in reducing the correlation between income inequality and energy efficiency. Therefore, accelerating industrialization, especially urbanization, can serve to reduce income inequality without sacrificing energy efficiency.
It is of great significance to explore the effect and mechanism of extreme climate on energy efficiency. In theory, extreme climate can stimulate the forcing effect to improve the level of innovation ability and energy efficiency in the long run. Empirically, this study measures the comprehensive level of extreme climate, innovation ability, and energy efficiency of 236 cities in China from 2011 to 2019 and then conducts econometric analysis. The results show that extreme climate significantly plays a forcing role for the improvement of energy efficiency. Innovation ability is also an important intermediary mechanism through which extreme climate influences energy efficiency. The impact of extreme climate on energy efficiency has significant regional heterogeneity, and the impact on peripheral cities is greater than that on core cities, showing the characteristics of a diminishing marginal effect. In order to better cope with extreme climate and improve energy efficiency, it is necessary to adhere to the combination of an effective market and promising government, increase financial investment in dealing with extreme climate, and promote energy transformation.
The proposal of a “dual-carbon” goal puts forward higher requirements for air pollution control. Identifying the spatial-temporal characteristics, regional differences, dynamic evolution, and driving factors of PM2.5 are the keys to formulating targeted haze reduction measures and ameliorating air quality. Therefore, adopting the Dagum Gini Coefficient and its decomposition method, the Kernel Density Estimation model, and spatial quantile regression model, this study analyzes the regional differences, dynamic evolution, and driving factors of PM2.5 concentrations (PM2.5) in the Yangtze River Economic Belt (YREB) and the upstream, midstream, and downstream (the three regions) from 2003 to 2018. The study shows that: (1) PM2.5 in the YREB was characterized by increasing first and then decreasing, with evident heterogeneity and spatial agglomeration characteristics. (2) Inter-regional differences and intensity of trans-variation were the primary sources of PM2.5 differences. (3) The density curve of PM2.5 shifted to the left in the YREB and the upstream, midstream, and midstream, suggesting that PM2.5 has declined. (4) Industrial service level (IS) and financial expenditure scale (FES) exerted a significant and negative effect on PM2.5 across the quantiles. On the contrary, population density (PD) showed a significant and positive influence. Except for the 75th quantile, the technology level (TEC) significantly inhibited PM2.5. The remaining variables had a heterogeneous impact on PM2.5 at different quantiles. The above results suggest that regional joint prevention and control mechanisms, collaborative governance mechanisms, and comprehensive policy mix mechanisms should be established to cope with PM2.5 pollution and achieve green, sustainable economic development of the YREB.
Improving energy efficiency is one of the most effective ways to address environmental constraints and achieve high-quality economic development. The existing literature suggests that technological progress has an important impact on energy efficiency, but ignores the role of different paths of technological progress. Herein, the impact of different technological progress paths on energy efficiency was analyzed using the dynamic panel data model and the threshold model based on the energy efficiency measurement using the Meta-frontier and Nonradial Directional Distance Function model. The findings of this study were as follows: (1) During the study sample period, a fluctuating upward trend was observed in China's energy efficiency, and there were significant differences in energy efficiency in different regions of China. (2) The energy efficiency in China could be significantly improved by domestic innovation and regional technology diffusion. Even though the imported technologies do not play a significant role in promoting energy efficiency and have no positive effect on the improvement of energy efficiency from the perspective of domestic technology absorption or foreign technology absorption. (3) The eastern and central regions of China improve their energy efficiency through domestic innovation. However, none of the technological progress paths in the western and low-energy-efficiency regions have played a positive role in promoting energy efficiency. (4) The effect of promoting domestic innovation and regional technology diffusion on energy efficiency is gradually increasing with human capital improvement, and the restriction of foreign technology import on energy efficiency has obviously decreased. In a nutshell, different paths of technological progress have differential effects on energy efficiency improvements. Based on the above results, this paper makes some targeted policy recommendations on the choice of technological progress paths to improve energy efficiency in different regions of China.
Achievement of carbon neutrality requires lowering energy consumption in China. However, only a few studies have focused on the impact of urban mining on the reduction in energy consumption, and the impact of urban mining on reducing energy consumption remains unclear. This study explored the impact of urban mining on energy efficiency by adopting the panel data of 281 prefecture-level cities in China between 2003 and 2016 using the difference-in-difference method, and tested the setting up of urban mining pilot bases on reducing urban energy consumption per unit of gross domestic product (GDP) and channel mechanisms. The empirical findings show that urban mining pilot bases have significantly reduced energy consumption per unit of GDP by 3.67%. The instrumental variable method was used to overcome endogenous problems of the empirical results and related robustness tests. The verification results of the impact mechanism show that urban mining reduces the energy consumption per unit of GDP through three channels: by improving the degree of urban marketization, enhancing the harmony between the government and the market, and increasing the abundance of factor markets. The heterogeneity analysis shows that urban mining has a significant reduction effect on energy consumption per unit of GDP in all four regions of China; however, the energy-saving effect in the northeast is higher than that in the other regions. This study provides an empirical test and policy insights for the contribution of urban mining pilot bases in promoting China’s development of green industry and circular economy. It also offers a new path for cities in developing countries to promote energy conservation and achieve urban sustainability via urban mining.
In the fight against global climate change, improving energy efficiency and reducing related emissions are critical to mitigating sustainability issues such as global warming. In this regard, technological progress is known to improve energy efficiency and reduce related emissions. However, previous sustainability studies on Africa have paid less attention to energy and emission efficiency convergence among countries, along with the contribution of technological progress to the continent's sustainability. Accordingly, this paper develops a novel framework that assesses energy and emission efficiency convergence, gauges technology inequality, identifies the barriers to technology diffusion, and explores the factors influencing energy and emission efficiency convergence in Africa. The proposed framework incorporates Data Envelopment Analysis (DEA), several economic convergence concepts, a K-means clustering model, the concept of efficiency Gini coefficient, and a group-based inequality decomposition method. We empirically covered 39 African countries from 2006 to 2018. The results show that African countries are, on average, 71.1% energy efficient and 65.3% CO2 emission efficient, leaving more room for energy saving and emissions reduction. Second, there is strong evidence of convergence in energy and emission efficiencies in Africa. Third, despite the observed convergence process, there is significant heterogeneity between two research groups (i.e., low-and high-efficiency groups of countries) in terms of energy and emission efficiencies, with persistent technology inequality suggesting a lower contribution of environmental technological advancements to the continent's sustainability. Furthermore, cross-group inequality is the primary barrier to technology diffusion. Fourth, regional cooperation accelerates the convergence process in the two efficiency indicators. While economic development, industrial structure, and renewable energy consumption (REC) tend to favor energy and emission efficiency growth, rising foreign direct investment (FDI) and energy prices have a negative impact. Meanwhile, the effects of energy prices, FDI, and REC on emission efficiency are insignificant. Based on our findings, we discussed policy implications to promote energy and emission efficiency convergence and narrow environmental technology gaps.
For economic activities, energy is a necessary component of production. As the world's largest energy consumer and carbon emitter, China faces greater resource constraints and environmental pressure in the background of global climate change, improving energy efficiency is the inevitable path for long-term development. However, the energy efficiency in China at this stage is relatively low, thus Improving energy efficiency is urgent. Based on the panel data from the database of Chinese industrial enterprises and the database of pollutant emissions of Chinese industrial enterprises between 2003 and 2012, this paper empirically conducts a thorough investigation on the nonlinear impact of spatial agglomeration and enterprises' energy efficiency, and further examine its heterogeneity and mechanisms. The results show that: (1) There is a significant U-shaped link between spatial agglomeration and enterprises' energy efficiency. (2) Based on agglomeration externalities, scale effect, learning effect, competition effect, and policy effect are the internal mechanisms through which spatial agglomeration impacts on enterprises' energy efficiency. (3) The effects of spatial agglomeration on enterprises' energy efficiency are influenced by technology level, energy consumption level, agglomeration patterns, and enterprise characteristics. Based on the empirical findings, we suggest that enterprises take full advantage of the benefits brought by spatial agglomeration, and that the government encourage the growth period of spatial agglomeration to become more profound, more synergetic, and more diverse.
Regional economy coordinated development (RECD) is an important symbol of social progress and an important guarantee of social harmony, political stability and economic sustainable development. Previous studies mainly focus on the relationship between digitalization and RECD, but they fail to investigate the RECD efficiency (RECDE) driven by urban digitalization. This paper characterizes and explores the RECDE in the Yangtze River Economic Belt (YREB) from 2011 to 2019 from the perspective of urban digitalization by constructing a novel evaluation indicator system and using an EBM model. Meanwhile, this paper analyses the regional differences, temporal–spatial evolution characteristics, and the influencing factors of urban digitalization driving RECDE via the methods of spatial autocorrelation, the Gini coefficient, kernel density estimation and a geo-detector. The results indicate that: (1) the RECDE of YREB is on the rise as a whole, but the efficiency level is low. The RECDE shows a stepwise increase in the order of upper, middle and lower reaches. The RECDE also shows obvious spatial differentiation characteristics. (2) From the perspective of temporal and spatial evolution, the RECDE has changed from a dispersed state to a contiguous agglomerated state as a whole. The average RECDE of the YREB has varied significantly over the years. High-efficiency cities are relatively dispersed, while low-efficiency cities are relatively concentrated. (3) Government intervention, physical capital level and economic development level are the primary influencing factors of RECDE. These results imply that the government should attach great importance to the role of digitalization in improving the RECDE. When formulating regional coordinated development policies, the government should first formulate differentiation strategies according to the economic development level of different cities, and then give full play to the promoting role of government intervention and digital capital investment.
As an important driving force of high-quality urban development, developing new-type urbanization plays an irreplaceable role in realizing urban sustainable development goals. The existing work has conducted extensive research on the impact of new-type urbanization. However, the underlying mechanism and empirical evidence of its impact on green total factor energy efficiency (GTFEE) have not been systematically studied. Therefore, based on the panel data at the prefecture-level city level, this paper uses a dynamic panel data model to empirically verify the impact of developing new-type urbanization on GTFEE. The current work demonstrates the following results. First, developing new-type urbanization shows a fluctuating growth trend, and it has significantly promoted GTFEE improvement. Second, considering the factors of urban heterogeneity, the results reveal that new-type urbanization has a heterogeneous impact on GTFEE of different cities. Furthermore, the analysis of impact mechanism indicates that the effects of industrial structure and technological innovation are crucial channels for new-type urbanization to affect GTFEE. The new-type urbanization can also positively regulate its relationship with GTFEE by interacting with industrial structure upgrading and technological innovation.
The Yangtze River Economic Belt (YREB) is one of the most energy-consuming regions in China, accounting for 36% of the nation's total energy consumption in 2019. The fundamental purpose of productive activities in production sectors is to meet the increasing consumption demand of the household sector. The household sector plays an important role in promoting energy consumption. Taking the household energy consumption in the YREB as the research object, we establish a hybrid multi-regional input-output model and analyze the drivers of the household energy consumption by using the structural decomposition analysis method. The results show that the energy intensity is the main factor inhibiting energy consumption while the energy structure and the urban-rural consumption per capita are the main factors promoting the energy consumption. Furthermore, we use the structural path betweenness method to identify the critical transmission sectors and the key pathways. It can be found that energy sectors, manufacturing sectors and transportation sectors play important roles in energy transfer. Among them, energy from sector S1 mainly flows to sector S4, energy from sector S2 mainly flows to sector S3, while energy from sector S3 and sector S4 mainly flows to manufacturing sectors. The above results are expected to provide strong evidence for energy consumption reform in the YREB.
Financial agglomeration (FA) may play an essential role in enhancing energy efficiency (EE) and, thus, is important from both theoretical and empirical viewpoints. However, few studies have investigated the causal nonlinear relationship between FA and EE. Hence, we first extend the novel ray slacks-based measure with global technology to evaluate the urban EE in China during 2003–2018. Next, we reexamine the nonlinear causality of FA on EE and then explore the underlying impact mechanism. The empirical results show that China’s urban EE is generally relatively low with distinct patterns of regional differences. Moreover, we find that the causal relationship between FA and EE follows an inverted U-shaped function rather than a linear one. FA promotes the improvement of EE only up to a certain threshold point, after which it reverses into an inhibitory effect. A further analysis based on the two-regime spatial Durbin panel model suggests that FA can indeed improve the EE of surrounding cities through positive externalities when the degree of FA in focal cities is not substantially greater than that in surrounding cities. However, when financial resources absorbed in certain focal cities become increasingly higher than that in most surrounding cities, the positive spillover effect would gradually disappear and even reverse into an undesirable siphon, thereby inhibiting the improvement of overall EE. These findings provide new insights for understanding the role of FA in sustainable development.
Urban agglomeration is an important means of collaborative regional development, and a critical organizational form for cities when their industrialization and urbanization develop to a certain stage. The green development of urban agglomerations is important for achieving coordinated, high-quality regional development. In light of this, this study established an input–output index system that considers technological innovation and environmental quality. Then, we used a super-efficiency epsilon-based measure model that included undesirable output to evaluate the green development efficiency of seven urban agglomerations in China: Beijing–Tianjin–Hebei, the Yangtze River Delta, the Pearl River Delta, Chengdu–Chongqing, the Yangtze River Midstream, the Guanzhong Plain, and the Central Plain. The spatiotemporal variability of the green development efficiency of each urban agglomeration was analyzed using kernel density estimation, the coefficient of variation, and the Theil index. Finally, a spatial econometric model was established to explore the factors affecting the green development efficiency of different agglomerations. The findings improve our understanding of the spatiotemporal patterns of green development in urban agglomerations, which can support the formulation of suitable strategies for collaborative regional green development.
The double-wheel-driven of manufacturing and producer services industrial co-agglomeration is of great significance for transforming the economic growth mode driven by a single industry, integrating and extending regional resources, and improving energy efficiency. Based on panel data from 2004 to 2019, this paper uses the spatial Dubin model to analyze the impact of industrial co-agglomeration on total factor energy efficiency (TFEE) and its regional heterogeneity; Moreover, the mediating model is employed to examine the mediating effect of green technological innovation in the industrial co-agglomeration affects TFEE. Last but not least, the threshold panel regression model is conducted to verify the nonlinear relationship between industrial co-agglomeration and TFEE. The results show that: There is a U-shaped curve relationship between industrial co-agglomeration and TFEE, namely that industrial co-agglomeration first shows a certain inhibitory effect on TFEE, and then plays a significant role in promoting. Moreover, there are obvious regional heterogeneities in the impact of industrial co-agglomeration on TFEE and its spatial spillover effect. Industrial co-agglomeration has a significant indirect impact on TFEE through green technological innovation. In addition, there is a single threshold effect in the impact of industrial co-agglomeration on TFEE, only when the industrial co-agglomeration degree crosses the threshold value of 0.6329, can it positively promote the improvement of TFEE.
This study puts forward a logical framework for green innovation network analysis, which includes a spatial dimension, a relational dimension, and a systems dimension. Here, we put forward some basic research ideas concerning the optimization and regulation of green innovation networks in terms of the systems dimension and we investigate the micro-dynamic mechanisms of green innovation network expansion using a spatial econometric model. Our main research results are as follows: The efficiency of green innovation in the Yangtze River Economic Belt has improved significantly, however, the gap between cities has gradually increased, and a problem of efficiency regression has emerged. The green innovation network has changed from the primary stage dominated by Edge Network to the rapid growth stage dominated by Supporting Network, and formed a complex network pattern with diversified hierarchical structure. Node symmetry is helpful in forming more extroverted connections and promoting the expansion of green innovation networks. Node proximity and connection symmetry inhibit the growth and development of networks, and knowledge flow cooperation networks can accelerate the evolution of green innovation networks. Finally, this paper holds that we should combine the actual development needs, emphasize the basic principles of differentiated development, and construct the development pattern of regional collaborative innovation. This can also provide a theoretical reference for enriching our understanding of green innovation networks while narrowing the gap between cities.
This study investigated the impact of industrial activity and energy structure on environmental pollution from the demand and supply side with the panel data source from 2000 to 2016 of 30 provinces in China. Empirical results show that, in the long-term, increasing R&D investment, improving industrial energy efficiency, and adjusting the coal consumption structure can reduce the pollution emissions significantly. However, the environmental pollution treatment investments and the capital-embodied technological progress have different impacts on various emissions. Specifically, the former reduces the total emissions but does not impact the emissions intensity; the latter, namely, capital-embodied technological progress, can reduce wastewater emissions because of the possible performance of capital quality upgrading. In the short-term, only coal consumption structure and capital-embodied technological progress have similar effects on the pollution emissions as they are in the long term, reflecting that the policymaking is different in the short-term and long-term. Furthermore, this study confirms that environmental regulations affect industrial pollution through energy efficiency and energy structure changes. The findings show that adjusting the energy structure is the primary channel to emissions reduction.
China is experiencing rapid economic development where outward foreign direct investment (OFDI) ranked third globally in 2017. OFDI could affect energy efficiency in China through reverse technology spillover. However, few studies have studied the impact of OFDI on total factor energy efficiency (TFEE), notably on the non-linear relationships and potential threshold level of the variables that would impact non-linearity. A panel smooth transition regression (PSTR) model was developed to analyse the non-linear impact of OFDI (explaining variable) on TFEE (explained variable). The data from 30 provinces, municipalities and autonomous regions (except Tibet) in China from 2006 to 2017 were considered. Industrial structure, opening-up level and human capital were selected as the transition variables to analyse potential threshold levels on how OFDI would enhance TFEE. The PSTR modelling results show that increasing OFDI in China can improve TFEE where the relationship is non-linear. The thresholds for opening-up level, industrial structure and human capital are 36.11%, 37.59% and 8.33%. With the change of the industrial structure, opening-up level and the human capital, the effect of OFDI on TFEE transited slowly and smoothly between high and low regimes. The opening-up level in Fujian, Zhejiang, Tianjin, Jiangsu, Guangdong, Beijing, and Shanghai is above the threshold level. Most regions show the industrial structure and human capital levels above the threshold. This study shows the importance of developing a non-linear model to accurately evaluating the impact of OFDI in enhancing TFEE in China. The model enables policymakers to assess the thresholds of the transition variables to observe the positive effect of OFDI on TFEE.
Despite China’s emissions having plateaued in 2013, it is still the world’s leading energy consumer and CO2 emitter, accounting for approximately 30% of global emissions. Detailed CO2 emission inventories by energy and sector have great significance to China’s carbon policies as well as to achieving global climate change mitigation targets. This study constructs the most up-to-date CO2 emission inventories for China and its 30 provinces, as well as their energy inventories for the years 2016 and 2017. The newly compiled inventories provide key updates and supplements to our previous emission dataset for 1997–2015. Emissions are calculated based on IPCC (Intergovernmental Panel on Climate Change) administrative territorial scope that covers all anthropogenic emissions generated within an administrative boundary due to energy consumption (i.e. energy-related emissions from 17 fossil fuel types) and industrial production (i.e. process-related emissions from cement production). The inventories are constructed for 47 economic sectors consistent with the national economic accounting system. The data can be used as inputs to climate and integrated assessment models and for analysis of emission patterns of China and its regions.
The promotion of industrial restructuring and technological innovation is the most important and realistic way of improving energy efficiency. This thesis uses the modified Super-SBM method to measure China’s total-factor energy efficiency and then uses the dynamic spatial panel model (DSPM) to verify the effect of industrial structure and technological innovation on total-factor energy efficiency. The study found that from 2003 to 2016, China’s total-factor energy efficiency showed a fluctuating trend of “falling first and then rising.” The inflection point appeared in 2012; total-factor energy efficiency in the Eastern region was significantly higher than the national average, while in the Central and Western regions, it was significantly lower. The results of the analysis show that both the service adjustment of the inter-industry structure and the productivity growth of the intra-industry structure significantly promote improvements in total-factor energy efficiency. However, due to the low conversion rate of scientific and technological achievements in China, the impact of technological innovation input on total-factor energy efficiency is not significant. This is in contradistinction to technological innovation output which does significantly improve total-factor energy efficiency. The above research conclusion is still robust and reliable after changing the measurement method and spatial weight matrix.
It is greatly important to promote low-carbon green transformations in China, for implementing the emission reduction commitments and global climate governance. However, understanding the spatial spillover effects of carbon emissions will help the government achieve this goal. This paper selects the carbon-emission intensity panel data of 11 provinces in the Yangtze River Economic Belt from 2004 to 2016. Then, this paper uses the Global Moran’s I to explore the spatial distribution characteristics and spatial correlation of carbon emission intensity. Furthermore, this paper constructs a spatial econometric model to empirically test the driving path and spillover effects of relevant factors. The results show that there is a significant positive correlation with the provincial carbon intensity in the Yangtze River Economic Belt, but this trend is weakening. The provinces of Jiangsu, Zhejiang, and Shanghai are High–High agglomerations, while the provinces of Yunnan and Guizhou are Low–Low agglomerations. Economic development, technological innovation, and foreign direct investion (FDI) have positive effects on the reduction of carbon emissions, while industrialization has a negative effect on it. There is also a significant positive spatial spillover effect of the industrialization level and technological innovation level. The spatial spillover effects of FDI and economic development on carbon emission intensity fail to pass a significance test. Therefore, it is necessary to promote cross-regional low-carbon development, accelerate the R&D of energy-saving and emission-reduction technologies, actively enhance the transformation and upgrade industrial structures, and optimize the opening up of the region and the patterns of industrial transfer.
As part of the country's efforts to achieve green development, China implemented a mandatory energy intensity reduction target in its 11th "Five-Year Plan (FYP)" in 2006, and then began to roll out a series of relevant measures. However, existing studies have paid little attention to the actual effects of China's energy intensity constraint policy (EICP). In this paper, using panel data from China's 36 industrial sub-sectors covering the years from 2001 to 2014, we adopt the difference-in-differences (DID) method to investigate for the first time the EICP's (marginal) effect on total factor energy efficiency growth (TFEEG). We also estimate the super-position effect caused by the introduction of a carbon intensity constraint policy (CICP) on TFEEG, through the difference-indifference in differences (DDD) strategy. Finally, using counterfactual, regrouping and quasi-DID analyses, we conduct a series of robustness tests of the empirical results. The results show that the TFEEG in China's industrial sector experienced an overall declining trend between 2001 and 2014. The implementation of the EICP has had a significantly negative effect on the improvement of the TFEEG of sub-sectors with higher levels of energy intensity. After the implementation of the EICP, the TFEEG rate of these sub-sectors declined by 4.31%, compared to the rate of the other sub-sectors. The results of a series of robustness tests indicate that such a negative effect is credible. The marginal effect in the first two years after the implementation of the EICP was significantly negative, while the superposition effect of the introduction of a CICP on industrial TFEEG remained negative. Thus, the Chinese government should reinforce the implementation of energy-saving policies by introducing additional market-oriented auxiliary policies to propel the green development transformation of China's industrial sector.
Energy cooperation has been emphasized strongly in the Belt and Road (B&R) initiative. Therefore, the energy efficiency of China has attracted much attention from experts. However, relevant studies are still insufficient. This paper analyzes the total factor energy efficiency (TFEE) and its influencing factors of 17 B&R key regions from 2005 to 2015. We use the ratio of target energy input and actual energy input to calculate the regional TFEE under environmental constraints. The Malmquist index and the Tobit model are applied to investigate the internal and external influences of TFEE. Measurement analysis shows that the TFEE of the B&R key regions has not improved in recent years and it is unbalanced during the study period. Regions in the east area have the highest TFEE; regions in the west area have the second high TFEE; and regions in the north area have the lowest TFEE. Regression analysis shows that for the B&R key regions, technical changes, coal consumption, research and development, and environmental pollution have mainly negative effects on TFEE; pure efficiency changes, scale efficiency changes, economic structure, opening up, and government finance have mainly positive effects on TFEE. Finally, precise policy implications are proposed.
Increased environmental pollution and energy consumption caused by the country’s rapid development has raised considerable public concern, and has become the focus of the government and public. This study employs the super-efficiency slack-based model–data envelopment analysis (SBM–DEA) to measure the total factor energy efficiency of 30 provinces in China. The estimation model for the spatial interaction intensity of regional total factor energy efficiency is based on Wilson’s maximum entropy model. The model is used to analyze the factors that affect the potential value of total factor energy efficiency using spatial dynamic panel data for 30 provinces during 2000–2014. The study found that there are differences and spatial correlations of energy efficiency among provinces and regions in China. The energy efficiency in the eastern, central, and western regions fluctuated significantly, and was mainly because of significant energy efficiency impacts on influences of industrial structure, energy intensity, and technological progress. This research is of great significance to China’s energy efficiency and regional coordinated development.
We empirically compare both the popular Luenberger indicator with the less popular Luenberger-Hicks–Moorsteen productivity indicator on an agricultural panel data set of Chinese provinces over the years 1997–2014. In particular, we test for the differences in distribution when comparing these indicators. These tests are crucial to answer the question to which extent the Luenberger indicator can approximate the Luenberger-Hicks–Moorsteen productivity indicator that has a Total Factor Productivity (TFP) interpretation.
Using energy efficiently has become top priority concern which requires an adequate policy reaction bearing in mind both energy conservation and efforts to combat adverse climate changes. The paper explored the total-factor energy efficiency and change trends in technical efficiency in the Croatian counties during the period 2001–2013. Employing data envelopment analysis, the overall technical, pure technical and scale efficiency are assessed. Considering the empirical results, we have concluded the following. Technical inefficiency is generated almost equally by the pure technical effect and an incorrect production scale. The overall geographical distribution of the technical efficiency scores points to the presence of spatial concentration, i.e., a dualistic pattern (centre vs. periphery) in the production process. The differences between the best practice and the worst technical efficiency scores indicate the presence of significant disparities among Croatian counties. The years with deteriorating electricity efficiency seem to coincide with the important economic/energy changes that happened in Croatia. Finally, subnational governments may play an important role in energy efficiency policies.
This paper constructs a three-stage Slacks-Based Measure (SBM) model to evaluate and analyze the total-factor energy efficiency (TFEE) of 276 cities in China during the period of 2000-2012 from the management and environment dual perspectives according to the principles of multi-stage Data Envelopment Analysis (DEA) model. In the first stage, a SBM-DEA model is applied to assess TFEE scores to illustrate the effects of the energy factors, while considering the undesirable output. In the second stage, we adjust the original data, and then in the third stage, we use SBM model again to get efficiency evaluation and obtain pure management efficiency of every decision unit. The results show that Chinese TFEE is still low and energy saving potential can be up to 34-46%, showing an inverted "U" shape tendency and characteristic of regional imbalance. Based on these findings, we further put forward some paths and strategies to improve Chinese energy efficiency.
This paper introduces a decomposition of the additively complete Luenberger-Hicks-Moorsteen Total Factor Productivity indicator into the usual components: technical change, technical inefficiency change and scale inefficiency change. Our approach is general in that it does not require differentiability or convexity of the production technology. Using a nonparametric framework, the empirical application focuses on the agricultural sector at the state-level in the U.S. over the period 1960 − 2004. The results show that Luenberger-Hicks-Moorsteen productivity increased substantially in the considered period. This productivity growth is due to output growth rather than input decline, although the extent depends on the convexity assumption of the technology. Technical change is the main driver, while the role of technical inefficiency change and scale inefficiency change also depends on the convexity assumption of the technology.
Productivity measures are increasingly regarded as key indicators of economic performance. Identifying sources of productivity growth is of interest to both firms and policy makers. This paper revisits the debate on how to decompose productivity growth into explanatory factors, with a focus on extracting technical progress, technical efficiency change, and returns to scale components. Using Bjurek’s concept of the Malmquist index, introduced into production theory in a systematic way by Caves, Christensen and Diewert, a reference technology is required to define the components of interest. Unlike other approaches, ours does not make any convexity assumptions on the reference technology but instead follows the example of Tulkens and his coauthors in assuming that the reference technology satisfies free disposability assumptions. A new decomposition of a productivity index is provided, with the existence and properties of the underlying distance functions of the decomposition proven under relatively unrestrictive assumptions. The paper also provides for the first time a theoretical justification for the geometric average form of the Bjurek productivity index. These rigorous theoretical contributions provide significant avenues for enhanced understanding of empirical productivity performance
Total-factor energy efficiency (TFEE) assessment has received increasing attention in both operations research and energy economics communities. Earlier TFEE studies implicitly assume that production activity lies in the economic area, which precludes the possibility that the production activity lies in the non-economic area and thus suffers from congestion. This paper contributes to develop TFEE index by taking into account congestion effect. It starts with the definition of congested production technology, based on which several data envelopment analysis models are proposed to construct TFEE index with congestion. The models for quantifying energy inefficiency caused by congestion effect are also developed. We apply the proposed index to evaluate the energy efficiency performance of Chinese industrial sectors at province level in 2010–2012. It is found that TFEE with congestion can yield useful insights about the choice of proper ways to achieve energy efficiency improvement. A comparison with the empirical results under congestion-free production technology indicates that ignoring congestion effect may lead to significantly different TFEE scores when congestion effect does exist.
Chinese industry has developed greatly since China implemented its “reform and opening-up” policy in 1978. With the rapid development of industry, the problems of growing energy consumption and environmental pollution are drawing increasing attention from government managers and scholars. This paper divides industrial systems into two stages, an energy utilization stage and a pollution treatment stage, for accurately evaluating the total-factor energy efficiency as well as the overall efficiency. We build a new two-stage data envelopment analysis model with shared inputs to open the “black box” of efficiency measurement in traditional energy efficiency methods. Applying the model to data for Chinese regions, we can display the advantages and disadvantages of these two stages of industry. The results show that (1) the performance of Chinese industry improved during the years 2006–2010; (2) the energy utilization stage performance was better than that of the pollution treatment stage, but the gaps reduced year by year; and (3) energy efficiency increased during this period. Based on these results, some policy recommendations are given.
Culture is an important factor affecting happiness. This paper examines the predictive power of cultural factors on the cross-country differences in happiness and explores how different dimensions of cultural indices differ in their effects on happiness. Our empirical results show that the global leadership and organizational behavior effectiveness nine culture indices are all significantly related with happiness. Out of these nine indices, power distance (PDI) and gender egalitarianism (GEI) play the most important and stable role in determining subjective well-being (SWB). We further examine the relative importance of the various variables in contributing to the R-squared of the regression. The results show that PDI is the most important, accounting for 50 % of the contributions to R-squared of all variables, or equalling the combined contributions of income, population density and four other traditional variables. The contribution of GEI is 37.1 %, also well surpassing other variables. Our results remain robust even taking account of the different data for culture and SWB.
This paper analyzes energy efficiencies of 29 administrative regions in China for the period 1995–2002 with a newly introduced index. Most existing studies of regional productivity and efficiency neglect energy inputs. We use the data envelopment analysis (DEA) to find the target energy input of each region in China at each particular year. The index of total-factor energy efficiency (TFEE) then divides the target energy input by the actual energy input. In our DEA model, labor, capital stock, energy consumption, and total sown area of farm crops used as a proxy of biomass energy are the four inputs and real GDP is the single output. The conventional energy productivity ratio regarded as a partial-factor energy efficiency index is computed for comparison in contrast to TFEE; our index is found fitting better to the real case. According to the TFEE index rankings, the central area of China has the worst energy efficiency and its total adjustmentof energy consumption amount is over half of China's total. Regional TFEE in China generally improved during the research period except for the western area. A U-shape relation between the area's TFEE and per capita income in the areas of China is found, confirming the scenario that energy efficiency eventually improves with economic growth.
Energy efficiency is tied to energy activities and environmental effects and serves as a useful tool for sustainability analysis. Few insights have been acquired for sustainability development from resource-based cities in developed or developing countries. A Super-Slack-Based Measure (Super-SBM) with undesirable outputs is established to account for the total-factor energy efficiency from an energy-economy-environment perspective. Using China as a case study, the spatial variation in energy efficiency from 104 resource-based cities is analysed, furthermore, the results are compared with a scenario that does not consider environmental constraints. Finally, resource-based cities are classified into three categories through K-means clustering technology: high-efficiency region, medium-efficiency region and low-efficiency region. The investigation results show the following: (1) Efficiency disparities exist in resource-based cities under different scenarios, as a whole, the energy efficiency in the scenario two considering by-products of energy activities is obviously lower, which can more truly represent the sustainability of resource-based cities. (2) Most resource-based cities are in low-efficiency zones with substantial room for improvement. Spatial agglomeration effect or spatial spillover effect appears in a few cities. (3) Urban development in developing countries may follow the full life cycle process of local resources. A total of 262 resource-based cities could be roughly categorized into four types. The energy efficiency of growing type is the highest, followed by grow-up type, recessionary type, and regenerative type. (4) The ordering of efficiency in resource-based city is as follows: oil and gas-based > multiple minerals-based > non-metallic-based > nonferrous metal-based > coal-based > forestry-based > ferrous metal-based. The discussion offered in this study for various types of resource-based cities could provide a reference for other cities or developing countries which are in similar industrialization phases and hope for sustainable development.
A comparison of the facts at the micro- and macro-level indicates that although some Chinese enterprises are at the international frontier in terms of micro-level energy efficiency, overall energy efficiency at the industrial and national level is seriously lagging behind. Hypotheses based on a market segmentation framework indicate that this is because market segmentation inhibits energy efficiency through its effect on technical efficiency, scale efficiency and allocation efficiency. This effect has been demonstrated by empirical research based on Chinese provincial panel data.
Based on the non-radial directional distance function of the Data Envelopment Analysis model, this paper uses the meta-frontier method to measure the total-factor energy efficiency of 30 provinces in China from 1997 to 2016. We then incorporate spatial correlation into the traditional convergence test model, study the spatial convergence of energy efficiency and explore the reasons for regional differences in energy efficiency. The results show that total-factor energy efficiency has significant regional heterogeneity, with the largest in the Eastern region, the second in the Central region and the smallest in the Western region. The root cause of energy inefficiency in China is poor management. As regional technology levels improve, the proportion of energy inefficiency due to management inefficiency slowly increases, while the proportion due to technology gap inefficiency gradually decreases. The total-factor energy efficiency has absolute β convergence and conditional β convergence within the three regions, but there is no absolute β convergence or conditional β convergence at the national level. This is mainly because the technology gap ratio between the Central and Western regions and the Eastern region has been gradually expanding. Not only do different factors have different effects on the convergence of energy efficiency but there are also significant differences within these three regions.
Technological innovation and energy efficiency are important indicators used to measure the success of the sustainable development strategy in China. This paper aims to explore the total factor energy efficiency (TFEE) at the city level in China and to evaluate the impact of technological innovation on TFEE. Therefore, a two-stage analysis was conducted for the period from 2001 to 2013. The first stage includes an estimation of TFEE scores using the Data Envelopment Analysis (DEA) methodology and Malmquist-Luenberger index, while the second stage includes an exploration of the impact of technological innovation on the TFEE scores obtained in the first stage using a system Generalised Method of Moment (GMM) regression analysis. Based on the results of the Malmquist-DEA, the TFEE of cities in China shows an upward trend overall, but obvious differences in the TFEE are observed among the four regions, with the highest TFEE observed in the eastern region, the second highest TFEE in the central region, a lower TFEE in the northeastern region and the lowest TFEE in the western region. The system GMM regression results reveal a significant positive impact of technological innovation on TFEE at the national level. According to the regional characteristics, the technological innovation in the eastern, western and northeastern regions is particularly important for improving TFEE, but technological innovation in the central region has inhibited the improvement of the TFEE. A logical response to these findings would be to develop different policies for different regions.
Different industries use energy with different levels of efficiency, even within the same industry. The biggest contributes of this study to the prior literature is that measured the energy efficiency by using coal consumption instead of energy consumption and explore the driving factors behind the variations in efficiency of different coal-intensive industries. The study selected 14 major Chinese coal-intensive industries and applied the super-efficiency model of data envelopment analysis to measure the total-factor energy efficiency of coal consumption (TFEEC) from 2006 to 2015. And the study used the Malmquist index to decompose the TFEEC and to analysis the variation situations of the TFEEC. The main results are as follows. (1) The TFEEC overall showed a trend of growth from 2006 to 2015, which showed that the energy saving and emission reduction policies should be further implemented, especially in developing innovative ways and policies to manage waste emissions. (2) In general, the factors causing the change of the TFEEC were mainly from technological progress, the government should encourage the enterprises to carry out energy technology innovation activities from both the energy-demand side and the supply side. However, the efficiency of different industries had different driving factor, the government should adopt different policies and measures to improve the TFEEC for different industries. (3) Half of the coal-intensive industries were still failing to efficiently utilize all of their resources, which showed that China still has large space to improve the TFEEC for different coal-intensive industries.
Climate change impacts, limited fossil fuel resources and escalating energy demand result in the use of clean and renewable energies such as solar thermal energy for sustainable agricultural production. In this study, the utilization of photovoltaic (PV) and photovoltaic/thermal (PV/T), which captures the remaining energy and removes waste heat from the PV module, is simulated by TRNSYS software as an alternative energy supplier in energy-environmental life cycle assessment (LCA) of greenhouse (GH) strawberry production in Alborz province, Iran. For this purpose, three scenarios, namely, present (Sc-1), PV (Sc-2) and PV/T (Sc-3) systems, are considered. Results show that the total input and output energy uses are examined to be 919250 and 142618.75 MJ ha−1, respectively, and diesel fuel with about 80% of the total energy usage is the most energy consuming input. Solar system simulation reveals 150 and 147 panels for PV and PV/T systems to supply energy, respectively. Environmental damages are investigated by IMPACT2002 + based on ten-ton of yield as the functional unit. LCA results indicate that diesel fuel and On-Farm emissions have most significant effects amongst damage categories in Sc-1. Moreover, applying solar technologies reduce total damage categories by about 16% and 6% in Sc-2 and Sc-3, respectively. The cumulative exergy demand (CExD) analysis indicates that diesel fuel is a significant portion of energy forms in Sc-1 and solar systems in Sc-2 and Sc-3 reduce the total CExD by about 50% and 33%, respectively compared to the Sc-1. Besides, Sc-2 with PV panels is the most energy-environmental-friendly scenario among them. It should be noted that Sc-3 has less efficiency compared to Sc-2 owing to additional equipment use and the temperate weather during the study. Finally, it can be concluded that trends of energy and environmental damage categories can be modified significantly by applying solar technologies.
This study takes the Yangtze River Economic Belt as a study area and analyzes the impacts of natural and socioeconomic factors on air pollution based on a dataset of urban air quality monitoring data in 2015 and meteorological and economic statistical data. We first apply the grey relational degree to test for the quantitative relationships between the natural and socioeconomic factors and air pollution. We then employ a novel method, specifically, the geographical detector, from the perspective of spatial stratified heterogeneity to reveal the potential impacts and interaction impacts of the natural and socioeconomic factors on air pollution. The results are as follows. (1) The grey relational degree results reveal that all factors in the topographical and meteorological layer, pollution sources layer, economic development layer, and urbanization layer have high relational degrees, indicating that these factors are closely correlated with air pollution. (2) The factor detector analysis reveals that the PM2.5 factor has the biggest q value, indicating that it is the primary contributor to air pollution, followed by PM10 and elevation. (3) The interaction detector analysis reveals that the interaction of two factors plays a more important role in influencing air pollution than does each factor individually. Moreover, the interactions between pair factors of pollution sources are the strongest. (4) The risk detector analysis reveals that elevation and precipitation are negatively correlated with air pollution, whereas pollution and urbanization factors are positively correlated with air pollution. (5) Finally, two leading impact areas for atmospheric pollution, namely, the Yangtze River Delta urban agglomeration and the Wuhan metropolitan area are predominantly attributed to the combination of natural and urbanization factors, whereas Yunnan and Guizhou are the least impact areas for atmospheric pollution because of their topographical and meteorological factors.
China's most important three urban agglomerations, the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD), are expected to play a leading role in energy saving and emissions reduction in China. Based on the biennial Malmquist productivity index and the non-radial direct distance function, this study improved the data envelopment analysis (DEA) model to evaluate the energy-saving and emissions-reduction performance (EEP) of these three urban agglomerations. The empirical results indicated that BTH did not show improvement in performance, whereas YRD and PRD showed a significant improvement. The annual growth rate of performance was higher in the PRD than in the other two urban agglomerations. The “catch-up effect” of the three urban agglomerations vanished after 2010, and the possible reasons for this change are the large-scale economic stimulus plan adopted by the government to address the global crisis and the recent decline of economic growth. The technical progress level increased at first, then decline, and increased again. This study also found that β convergence of performance existed in BTH and YRD. However, there was no β convergence of performance in PRD.
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.
This article reconsiders the way metafrontiers and associated measures of efficiency are obtained from nonparametric estimates of underlying group-specific frontiers. Both convex and non-convex metasets have been applied, but the large majority of articles applying this popular methodology assume that the metafrontier envelops a convex metaset. We argue that associated estimates of efficiency are potentially unreliable. We develop a refined methodology for nonparametric envelopment of non-convex metasets. We apply our methodology to a secondary data set to illustrate the potential errors associated with the currently established methods. Anticipating our main conclusion, we find that the convexification strategy consisting in assuming a convex metaset generally leads to erroneous results.
The increasing concerns on both energy supply security and environmental issues promote the global awareness in renewable energy (RE). European Union (EU) is among the most vulnerable countries/unions due to her high-energy import dependency and scarcity in energy reserves. This study first empirically confirms the substitution effect of RE for energy imports, then benchmarks the performance of the selected EU countries in RE efficiency and productivity from 2004 to 2014 with a perspective of energy security. The results of the super-efficiency model of data envelopment analysis (DEA) reveal that the average efficiency of the selected EU countries is increasing during the analysis period. While Sweden, Germany, Spain, Belgium and Romania are among the efficiency leaders, conventional energy producers limit the RE in France and United Kingdom (UK). The results of the sequential Malmquist-Luenberger Index analyses show that average total factor productivity of the group has increased by 8.4% annually, where technological change (innovation) is the prime driver of the productivity growth. The findings of this study not only highlight the convergence within EU towards the common objectives for RE and energy security, but also the technological diffusion and knowledge spillover. The authors believe that the techniques of total factor productivity and efficiency analyses have crucial roles in analyzing the renewable energy efficiency levels and energy policies of the countries.
In recent years, urban energy efficiency has received much attention from researchers. However, ignorance of the competitive relationships among regions results in inaccurate energy efficiency. Game cross-efficiency data envelopment analysis (DEA), which can account for the competitive relationships, is a reliable method to measure energy efficiency. First, this paper applies game cross-efficiency DEA to analyze the urban total factor energy efficiency (UTFEE) of 26 Chinese prefectural-level cities from 2005 to 2015 under environmental constraints. Then, a comparative analysis and a concrete analysis are conducted based on the urban energy efficiency. Finally, a Tobit model is used to examine the effects of 10 potential influence factors. The measurement results show that the urban energy efficiency considering the competitive relationship is lower than the traditionally calculated efficiency. Additionally, the urban energy efficiency did not improve during the research period. Furthermore, regression analysis suggests that economic development and city scale can promote urban energy efficiency, while government expenditure, industrial structures, energy prices, foreign investments, research investments and production endowment have negative impacts on urban energy efficiency. According to our results, some implications of theory and practice are discussed for researchers and policy makers.
This paper considers energy as a basic resource which together with capital and labor contributes to output creation. It aims to explore whether European Union (EU) regions use these production factors in an efficient way as well as to assess the impact of various environmental variables on technical and energy efficiency. To that end, a two-stage analysis was conducted for the period 2005–2013. The first stage included an estimation of pure technical and total factor energy efficiency (TFEE) scores using the data envelopment analysis (DEA) methodology, while the second one included an exploration of the factors affecting efficiency scores obtained in the first stage by using random-effects panel Tobit regression.
The results of DEA show that regional differences in technical and energy efficiency are considerable, whereby most of EU regions failed to utilize efficiently all of their resources. More developed EU regions are more technically and energy efficient. Moreover, most of EU regions experienced deterioration in TFEE in the recession years. The results of Tobit regression analysis, showing that there is a difference in the determinants of technical and energy efficiency, point out that human capital and innovation are particularly important for improving the region's efficiency or ecological performance.
Improvement of coal consumption efficiency is very important both for China in solving its problems of energy security and environmental pollution and for the world in addressing the issue of greenhouse gas emissions. Using a total-factor framework, this paper employs a data envelopment analysis (DEA) approach to evaluate the coal consumption efficiency of six energy intensive sub-industries in China in 2015. Coal consumption efficiency is factorized into economic efficiency and environmental efficiency, with sulfur dioxide, nitrogen oxide, and industrial smoke, dust and soot emissions treated as undesirable outputs of energy consumptions. The results show the following. (1) Of the six energy intensive sub-industries studied, two exhibit both DEA-effective coal economic efficiency and coal environmental efficiency. Coal economic efficiency is found to be greater than coal environmental efficiency, indicating that the benefits of economic efficiency have masked the negative effects of the environmental impact. China should therefore pay more attention to the clean utilization of coal. (2) Low environmental efficiency mainly resulted from the joint effects of pure technical efficiency and scale efficiency; therefore, enterprises in these industries must engage in management efforts to improve their ability to manage large enterprises. (3) The energy intensive industries characterized by coal environmental efficiency that failed to achieve pure technological effectiveness all exhibited input redundancy or output inadequacy of varying degrees while showing a potential for energy conservation and emissions reductions.
This study uses the dynamic DEA model to evaluate inter-temporal efficiency for executive efficiency based on fossil-fuel CO2 emissions in OECD countries and China. The significant difference between this study and previous studies is the assumption of energy stock, defined as a carry-over intermediate linking different terms. This model provides a ratio as a standard for energy stock to be adjusted, which is based on the assessment of the optimal quantity of energy stock. In addition, we explore output and input inefficiency indicators in the model to figure out the sources of operational inefficiency.
Regional energy and environmental efficiency measurement is a noteworthy research topic regarding regional development. Data Envelopment Analysis is a suitable technique in the studies of energy and environmental efficiency. This study investigates the efficiency and total-factor energy efficiency scores of the manufacturing industry in 26 regions of Turkey between the years 2003 and 2012, using four data envelopment analysis models supported by a total-factor framework. The first and the second models are based on absence and presence of undesirable outputs, respectively; the third model and the proposed new model aims to maximize energy saving potential considering undesirable outputs. The empirical results show that TR10-Istanbul region is the best performer and acts as a model for inefficient regions with its production composition. Total electricity saving potential is investigated per each region and for the manufacturing industry per each year between years 2003-2012. It is observed that Turkish manufacturing industry has an average electricity saving potential of 39.7%, which reaches its highest in 2004 and lowest in 2012. Another important finding of this study is the existence of a U-shaped relationship between gross value added per capita as regional development indicator and efficiency as well as total-factor energy efficiency index.
The index of TFEE (total-factor energy efficiency) is used to assess the level of energy consumption to produce economic output (GDP (gross domestic product)) based on multi factors input, which is superior to conventional energy efficiency evaluation regarded as a partial-factor energy efficiency index. The objective of this study is to provide the changes of TFEE at sector and provincial level and to illustrate the drivers behind such various changes in China. The results show that the TFEE of most industrial sectors in the eastern provinces is higher than that in other provinces. The most important finding is that the gap of TFEE across sectors was narrowed in the eastern provinces and expanded comparatively in the central and western provinces. Such result implies that the gap reduction of TFEE across sectors would be one of the important drivers behind the increase of overall TFEE. Meanwhile, the Tobit regression results indicate that technology progress, energy price and economic development have positive influence on TFEE. And the impact of technology progress is found to be of the most significance.
h i g h l i g h t s This study compares Japan with other developed countries for energy efficiency at the industry level. We compute the total-factor energy efficiency (TFEE) for industries in 14 developed countries in 1995–2005. Energy conservation can be further optimized in Japan's industry sector. Japan experienced a slight decrease in the weighted TFEE from 0.986 in 1995 to 0.927 in 2005. Japan should adapt energy conservation technologies from the primary benchmark countries: Germany, UK, and USA.: Data envelopment analysis (DEA) Total-factor energy efficiency (TFEE) Industry-level analysis Japan a b s t r a c t Japan's energy security is more vulnerable today than it was before the Fukushima Daiichi nuclear power plant accident in March 2011. To alleviate its energy vulnerability, Japan has no choice but to improve energy efficiency. To aid in this improvement, this study compares Japan's energy efficiency at the industry level with that of other developed countries. We compute the total-factor energy efficiency (TFEE) of industries in 14 developed countries for 1995–2005 using data envelopment analysis. We use four inputs: labor, capital stock, energy, and non-energy intermediate inputs. Value added is the only relevant output. Results indicate that Japan can further optimize energy conservation because it experienced only a marginal decrease in the weighted TFEE, from 0.986 in 1995 to 0.927 in 2005. To improve inefficient industries, Japan should adapt energy conservation technologies from benchmark countries such as Ger-many, the United Kingdom, and the United States.
A primal index of productivity change is introduced which decomposes exactly into three components: technical change, technical efficiency change and average scale economies (radial scale change). The productivity index is defined using variations of the distance function along pre-assigned input–output rays and, for this reason, it is deemed a radial productivity index (RPI). It is proven that: first, the RPI index collapses to the Malmquist productivity index when the technology is constant returns to scale (CRS); second the RPI index equals the Hicks-Moorsteen productivity index under homotheticity of technology (and non-CRS). The key to these results is a new definition and measure of the contribution of scale economies to productivity change.
Total factor productivity (TFP) can be defined as the ratio of an aggregate output to an aggregate input. This definition naturally leads to TFP indexes that can be expressed as the ratio of an output quantity index to an input quantity index. If the aggregator functions satisfy certain regularity properties then these TFP indexes are said to be multiplicatively
complete. This paper formally defines what is meant by completeness and reveals that (1) the class of multiplicatively complete TFP indexes includes Laspeyres, Paasche, Fisher, Törnqvist and Hicks-Moorsteen indexes, (2) the popular Malmquist TFP index of Caves et al. (Econometrica 50(6):1393–1414, 1982a) is incomplete, implying it cannot always be interpreted as a measure of productivity change, (3) all multiplicatively complete TFP indexes can be exhaustively decomposed into measures of technical change and efficiency change, and (4) the efficiency change component can be further decomposed into measures of technical, mix and scale efficiency change. Artificial data are used to illustrate the decomposition of Hicks-Moorsteen and Fisher TFP indexes.
China becomes the largest energy consumer in 2010 but its energy productivity is well below the world average. To meet China’s fast growing energy using, energy efficiency should be especially emphasized under China’s energy policy. This paper focuses on the regional level of energy efficiency change in China. And we analyze total factor energy efficiency for 30 Chinese provinces over the period 1998–2009 using Malmquist index method and Tobit analysis. The Malmquist estimation results suggest there is a dropping change trend of energy productivity growth. Chinese energy efficiency still faces with huge regional disparity, but the energy technical efficiency reflects convergence in the nationwide and west region. As a result of Tobit regression, we find that industrial structure, energy consumption structure and institutional factor have different influences on energy efficiency.
This study provides a no-output growth model to conveniently calculate the total-factor energy efficiency (TFEE) index originally proposed by Hu and Wang (2006). The TFEE index serves as a very well-known and popular means of estimating overall energy efficiency. While many previous studies have used the indicator of energy inefficiency, including the indicator of energy intensity (i.e., Energy input/Gross Domestic Product (GDP)) to measure energy efficiency, Hu and Kao (2007) point out that the indicator of energy intensity is not only a partial-factor energy efficiency indicator, but that this partial-factor ratio is also quite inappropriate for analyzing the impact of changing energy use over time. The TFEE index overcomes the disadvantage of the indicator of energy intensity as mentioned above, but five steps are needed to calculate the TFEE score. In this study, we provide a no-output growth model to conveniently calculate the TFEE score. Furthermore, we extend this no-output growth model to an output growth model. This study concludes that the output growth model not only makes it easier to calculate the TFEE index than the model proposed by Hu and Wang (2006) and Hu and Kao (2007), but that it can also obtain better TFEE scores.
This paper uses a total-factor framework to investigate energy efficiency in 23 developing countries during the period of 1980-2005. We explore the total-factor energy efficiency and change trends by applying data envelopment analysis (DEA) window, which is capable of measuring efficiency in cross-sectional and time-varying data. The empirical results indicate that Botswana, Mexico and Panama perform the best in terms of energy efficiency, whereas Kenya, Sri Lanka, Syria and the Philippines perform the worst during the entire research period. Seven countries show little change in energy efficiency over time. Eleven countries experienced continuous decreases in energy efficiency. Among five countries witnessing continuous increase in total-factor energy efficiency, China experienced the most rapid rise. Practice in China indicates that effective energy policies play a crucial role in improving energy efficiency. Tobit regression analysis indicates that a U-shaped relationship exists between total-factor energy efficiency and income per capita.
Data envelopment analysis (DEA) has recently gained popularity in energy efficiency analysis. A common feature of the previously proposed DEA models for measuring energy efficiency performance is that they treat energy consumption as an input within a production framework without considering undesirable outputs. However, energy use results in the generation of undesirable outputs as by-products of producing desirable outputs. Within a joint production framework of both desirable and undesirable outputs, this paper presents several DEA-type linear programming models for measuring economy-wide energy efficiency performance. In addition to considering undesirable outputs, our models treat different energy sources as different inputs so that changes in energy mix could be accounted for in evaluating energy efficiency. The proposed models are applied to measure the energy efficiency performances of 21 OECD countries and the results obtained are presented.
With minimal sleight of hand, it is possible to transform the recent growth experience of the People's Republic of China from the extraordinary into the mundane. Systematic understatement of inflation by enterprises accounts for 2.5 percent growth per year in the nonagricultural economy during the first two decades of the reform period (197898). The usual suspects (i.e., rising participation rates, improvements in educational attainment, and the transfer of labor out of agriculture) account for most of the remainder. The productivity performance of the nonagricultural economy during the reform period is respectable but not outstanding. To the degree that the reforms have improved efficiency, these gains may lie principally in agriculture.
Introducing a new difference-based Luenberger-Hicks-Moorsteen productivity indicator, this contribution establishes theoretically its relations with some existing ratio- and difference-based productivity indexes and indicators. The first main result is an approximation proposition stating that the logarithm of the Hicks-Moorsteen productivity index is about equal to the Luenberger-Hicks-Moorsteen productivity indicator. Secondly, we also establish the specific conditions under which the Luenberger-Hicks-Moorsteen indicator equals the recently introduced Luenberger indicator and compare these to the conditions governing the relations between ratio-based Hicks-Moorsteen and Malmquist indices. Copyright Springer-Verlag Berlin/Heidelberg 2004
This note suggests a new way of determining the exact contributions of the explanatory variables to the R-Square of a linear regression. The proposed methodology combines the so-called Shapley approach (Chantreuil and Trannoy, Inequality decomposition values: the trade-off between marginality and consistency. THEMA Discussion Paper, Université de Cergy-Pontoise, France 1999; Shorrocks, Decomposition Procedures for Distributional Analysis: A Unified Framework Based on the Shapley Value (mimeo), University of Essex, 1999) with the Fields (Res. Labor Econ., 22:1–38, 2003) decomposition. Copyright Springer Science+Business Media, Inc. 2007
This contribution establishes, from a theoretical viewpoint, the relations between the Malmquist productivity indices, that measure in either input or output orientations, and the Luenberger productivity indices, that can simultaneously contract inputs and expand outputs, but that can also measure in either input or output orientations. The main result is that a Malmquist productivity index overestimates productivity changes, since it provides productivity measures that are nearly twice those given by the Luenberger productivity index looking for simultaneous contractions of inputs and expansions of outputs. This relationship is empirically illustrated using data from 20 OECD countries over the 1974–97 period.
The definitions of the Malmquist output and input quantity indexes specified by D. W. Caves, L. R. Christensen, and W. E. Diewert (1982) are applied in this study. Based on these indexes, a 'Malmquist total factor productivity index' is derived for general production structures. The definition maintains the fundamental characteristic of a productivity index as a ratio between an output quantity change index and an input quantity change index. This index provides a remedy for the shortcoming of the traditional definition of the Malmquist productivity index in that the latter does not correctly measure changes in productivity in the presence of changes in returns to scale. Copyright 1996 by The editors of the Scandinavian Journal of Economics.