Total-factor Energy Productivity Research Articles

The Relationship Between Green Finance, Sustainable Technological Innovation and Energy Efficiency

Sustainable technological innovation can promote this progress in finance. This optimization and upgrading of energy efficiency can provide a good energy environment for green development. The collaborative innovation of financial enterprises has brought such development to the tertiary industry, driving the upgrading of consumer demand, and thus promoting the development of green finance. At this same time, sustainable technological innovation will increase investment in environmental protection, reduce energy consumption, promote the reduction of carbon emissions in the financial industry, and accelerate the development of green finance. This paper uses the data envelopment analysis method to calculate the Marquis production efficiency index, examines the dynamic generation efficiency of intertemporal energy input and output, and calculates the energy efficiency of green finance. This article measures and deconstructs energy efficiency at the provincial level, and pays attention to its changes. The study depicted the efficiency distribution of 28 provinces in two stages of the survey period. By using random kernel estimation for two periods during the survey period, dynamic distribution three-dimensional maps and density contour maps of total factor energy productivity, energy utilization efficiency, and energy allocation efficiency growth rates were drawn for the two periods. The results show that the transfer probability group of energy efficiency and its decomposition terms mainly falls near the diagonal, indicating that TFP and its decomposition term growth rate have certain transferability. From 2005 to 2015, the energy utilization efficiency of the seven economic regions has been significantly improved, and the energy efficiency differences in different regions have gradually converged. The energy efficiency of the Yangtze River Delta and the Pearl River Delta is the highest and has been continuously improved, followed by the Beijing Delta, the central region, the northeast region and the southwest region are again in terms of energy efficiency. Compared with provinces with relatively poor industrial structures, provinces with better industrial structures do not have significant advantages in energy efficiency, while provinces with higher levels of technological innovation typically have relatively higher energy efficiency.

How does industrial intelligence affect total-factor energy productivity? Evidence from China’s manufacturing industry

The flourishing industrial intelligence profoundly impacts manufacturing production, while empirical studies about the nexus between industrial intelligence and energy issues remain insufficient. This study evaluates total-factor energy productivity based on the stochastic frontier analysis (SFA) approach and then analyzes the energy productivity effects of industrial intelligence by employing the panel data of China’s manufacturing industry. We find that the total-factor energy productivity shows vast regional differences, and most regions have experienced a decline. Additionally, industrial intelligence significantly improves total-factor energy productivity, and the core results still hold after a set of robustness tests. Mechanism tests reveal the positive effects are mainly due to technological change and industrial structure upgrading. Heterogeneity analysis indicates that the impact of industrial intelligence is more pronounced in regions with higher economic development, more advanced information technology, and greater human capital. These findings provide insightful references for further intellectualization and carbon reduction in China’s manufacturing industry.

Driving green transformation: Innovations and green innovations in natural resource markets

The worldwide desire for green development necessitates that China's accelerating economic expansion be supported by environmental conservation. The present research established a connection between Green Total Factor Energy Productivity (GTFEP), Economic Opening-up (EOU), Environmental Regulations (ER), Rent of Natural Resources (RNR), and Green Innovation (GI) in China from the vantage point of environmental improvement. This study analyzed the short- and long-run dynamics among significant components using annual data from 1990 to 2022 and a bound testing technique for the Auto-regressive Distributed Lag framework (ARDL) with an error-correcting framework. The Granger causality study proved unidirectional causation from independent factors to GTFEP, and the findings confirmed cointegration between GTFEP and all other variables. In conclusion, the results showed that EOU, ER, RNR, and GI were all effective on China's GTFEP throughout the long and short term. As a result, government initiatives need to prioritize openness, environmental control, and eco-friendly technological development.

The Energy-Saving Effect of Tax Rebates: The Impact of Tax Refunds on Corporate Total Factor Energy Productivity

This paper investigates whether and how tax and fee support policies at the firm level in China influence the total factor energy productivity of enterprises. Using panel data from Chinese public trading companies for the period 2004–2020, this study employs a panel model for estimation. The findings suggest that tax rebates contribute to the improvement of the total factor energy productivity of enterprises. Specifically, a 1% increase in tax refunds leads to a growth of approximately 0.008% in total factor energy productivity. Robustness tests and endogeneity checks confirm the validity of the results. Heterogeneity analysis reveals that tax rebates have a significant impact on state-owned enterprises, small- and medium-sized enterprises, and non-technology firms in terms of enhancing their total factor energy productivity. Mechanism analysis indicates that tax rebates facilitate firms in alleviating financing constraints and enhancing their innovation capabilities, thereby improving energy efficiency. The research findings of this paper provide empirical support for optimizing policy supply, improving energy usage efficiency, and promoting the development of a globally sustainable economy.

Developing nexus between economic opening-up, environmental regulations, rent of natural resources, green innovation, and environmental upgrading of China - empirical analysis using ARDL bound-testing approach

With increasing demand for green growth globally, the escalating economic growth of China is also needed to be accompanied with environmental protection. From an environmental upgrading perspective, current study developed a nexus amid Green Total Factor Energy Productivity (GTFEP), Economic Opening-up (EOU), Environmental Regulations (ER), Rent of Natural Resources (RNR) and Green Innovation (GI) of China. Using annual data from 1990 to 2020 and applying a bound testing approach for Auto-regressive Distributed Lag model (ARDL) along with an error correction model, current study investigated short and long run dynamics amid variables of concern. The results confirmed cointegration between GTFEP and all other variables, and Granger causality analysis revealed uni-directional causality, running from independent variables towards GTFEP. Concluding the results, both long and short-run findings suggested effectiveness of EOU, ER, RNR and GI on GTFEP of China, and the results are statistically significant. Thus, the policies should be aimed to improve opening-up, environmental regulations and green innovation.

Does artificial intelligence improve energy productivity in China's industrial sector? Empirical evidence based on the spatial moderation model

This study investigates the impacts of industrial intelligence on the total factor energy productivity (TFEP) using extended STIRPAT and spatial moderation models for China's industrial sector. The results show that TFEP and industrial intelligence are both increasing, and industrial intelligence positively affects TFEP, for every 1% increase in industrial intelligence will lead to TFEP growth by 0.121% in the study area and 0.031% in surrounding areas. Environmental regulation, industrial upgrading, and advanced human capital all function as helpful moderators between industrial intelligence and TFEP, that is, for every 1% increase in environmental regulation, industrial upgrading, and advanced human capital, the growth of TFEP caused by industrial intelligence enhanced by 0.003%, 0.009%, and 0.022% in study area and 0.005%, 0.042%, and 0.054% in surrounding areas.

Does green technology innovation contribute to energy efficiency improvement: Empirical evidence from Chinese cities

To achieve carbon neutrality, energy efficiency improvement is an essential step. This paper measures the Malmquist energy productivity index (MEPI) to model the dynamics of total factor energy productivity utilizing the stochastic frontier analysis (SFA) method. We found evidence that city-level total factor energy efficiency (TFEE) has been greatly improved in China. However, there are significant regional differences among different cities. Then, this paper empirically examines the heterogeneous impacts of green technology innovation (GTI) on energy efficiency, and takes environmental regulation into consideration. The results show that GTI contributes to improving energy efficiency in cities with higher environmental regulations. Further, green innovation mainly improves the total factor energy productivity through technological progress instead of the catch-up effect.

The Impact of Economic Policy Uncertainty on Regional Total-Factor Energy Productivity in China.

This paper investigates the effect and possible mechanism of implicit macroeconomic policy uncertainty on regional energy productivity in China. This study takes the unexpected output of environmental pollution from energy consumption into account and uses the DEA-SBM method to measure the regional total-factor energy productivity (RTFEP) of prefecture cities in China from 2003 to 2017. Based on the economic policy uncertainty (EPU) index constructed by Baker et al. this paper estimates the effects of EPU on RTFEP and finds that there is a significant negative relationship between them. Specifically, for every unit increase in EPU, RTFEP will decrease by 5.7%. This paper further examines the mechanism of EPU on RTFEP from the perspective of the market and the government and finds that EPU exerts a restraining effect on RTFEP by affecting the consumption structure of the energy market and government intervention in the economy. In addition, the results show that there is heterogeneity in the impact of EPU on RTFEP which varies between resource-based cities at different development stages and different dominant resources. Finally, this paper proposes to cope with the negative impact of EPU on RTFEP by optimizing the energy consumption structure, regulating government investment areas, and transforming the economic development mode.

Moving towards Environmental Sustainability: Can Digital Economy Reduce Environmental Degradation in China?

In the context of environmental sustainability and accelerated digital technology development, China attaches great importance to the prominent role of digital economy in addressing environmental degradation. Utilizing Chinese provincial panel data from 2011 to 2019, this study investigates whether the digital economy can improve China's environmental sustainability proxy by reducing carbon emission intensity. Based on the fixed effects model, the findings reveal that the digital economy has a significant negative effect on carbon emission intensity and the conclusion remains robust after conducting several robustness checks. However, this impact shows regional heterogeneity, which is more effective in resource-based eastern regions and the Belt and Road provinces. Moreover, mediating effect analyses indicate that the transmission mechanisms are energy consumption structure, total factor energy productivity, and green technology innovation. Furthermore, the results based on the spatial Durbin model (SDM) demonstrate that digital economy development has a significant spatial spillover effect. Finally, on the basis of results analysis and discussion, policy recommendations are provided for achieving environmental sustainability.

Pathway to green growth: A panel-ARDL model of environmental upgrading, environmental regulations, and GVC participation for the Chinese manufacturing industry

Since the opening up of China, its manufacturing sector contributed significantly toward economic development on one hand and caused elevated environmental pollution and heavy resource consumption problems on the other hand. Green productivity is an essential means of addressing environmental problems and promoting sustainable environmental performance and long-term economic growth. When the factors affecting the green total factor energy productivity (GTFEP) are studied in recent years, the hitherto literature lacks the relationship between GTFEP, environmental regulations, and global value chain (GVC) participation. Using GTFEP as a proxy of environmental upgrading for the Chinese manufacturing industry, this study investigates the nexus between the GTFEP, environmental regulation index (ERI), and total production length index (TPLI). Based on panel data 18 industries from 2000 to 2014, the current study employed the panel pool mean group estimators for the auto-regressive distributed lag model (ARDL) and estimated the long-run relationship between variables. Findings revealed the positive effect of ERI and GVC participation (TPLI) on the GTFEP of the Chinese manufacturing industry. Furthermore, pairwise panel causativeness analysis indicated unidirectional causality between GTFEP and ERI, running from GTFEP to ERI. Based on research findings, some conclusions and policy implications are derived, such as strict enforcement of environmental regulations, industry-specific policies, promotion of clean energy resources, and acceleration of the country’s further opening-up and reforms. All of these will help promulgate the GTFEP and environmental upgrading of the Chinese manufacturing sector, thus leading to sustainable economic development decoupled from environmental pollution.

The Impact of Financial, Economic and Environmental Factors on Energy Efficiency, Intensity, and Dependence: The Moderating Role of Governance and Institutional Quality

Economies are under serious pressure to sustain themselves due to globalization, focusing simply on economic growth and operational efficiency will not yield the desired sustainable financial and economic position for economies. Management of energy efficiency and reducing the energy dependence and intensity is the core objective for the economy and achievement of the above objective financial, economic, and environmental factors need to be studied. Economic wellbeing critically depends on the efficient use of energy and which type of governance mechanism is in place will also define the ways toward energy efficiency. A better understanding of the relationship will help the economies to fulfill their energy needs efficiently, realize developmental goals, and overcome environmental issues. This study examines the relationship between financial, economic, and environmental factors with energy efficiency, intensity, and dependency with moderating role of governance including institutional quality and governance index for belt and road initiative countries. The core objective of the study is to analyze which financial, economic, and environmental factors serve well in the management of energy efficiency, intensity, and dependence issues and how various dynamics of governance policies including market structure moderate the above-mentioned relationship. For this secondary data is used from world development indicators, market insiders, and Chicago Board Options Exchange (CBOE) data. This research will help the researchers and practitioners to achieve long-term economic, financial, and environmental sustainability. The proposed model predicts that 0.44% change in Total Factor Energy Productivity measure of Energy Efficiency, 0.03% changes in Energy use/Purchasing Power Parity ratio measure of Energy Intensity, and 9.63% changes in Energy Reserves/Energy Production ratio measure of Energy Dependence. Results also reveal that environmental factors including Rural population, Urbanization, Co2 emission, energy use, and energy production will contribute most to achieving sustainable economic growth.

Total factor energy productivity considering undesirable pollutant outputs: A new double frontier based malmquist productivity index

Determining energy productivity change during a time interval is an important issue in many production lines. Data Envelopment Analysis (DEA) approach is a well-known technique utilized to measure productivity change and widely used by researchers to analyze the performance of decision making units. In this regard, the modified Enhanced Russell Measure (ERM), a non-radial DEA-based efficiency model, is applied to develop new models for measuring the Malmquist productivity index (MPI). To present productivity changes of decision making units (DMUs) over time more truly and more comprehensively than the conventional MPI method, this paper proposed three new approaches by using optimistic, pessimistic, and general viewpoints of data envelopment analysis. However, in many production processes, undesirable outputs such as smoke or waste pollution may be generated. Thus, this paper has further developed the proposed approaches in the presence of an undesirable output. The proposed methodology is applied to evaluate the productivity changes and efficiencies of chickpea production farms in 16 provinces in Iran.

Impact assessment of clean air action on total factor energy productivity: A three-dimensional analysis

Energy is the engine of China's rapid economic growth. Whereas, under the requirements of economic transition, energy productivity is in urgent need of improvement. To curb air pollutant emission, the Chinese government promulgated the Clean Air Action Plan in 2013, and the policy puts forward specific measures on energy utilization. How does energy productivity vary before and after policy implementation? Can environmental regulations promote energy productivity? Based on three-dimensional panel data—time (2008–2017), industry (19 2-digit industrial sectors), and region (30 provinces), this paper uses stochastic frontier analysis to measure total factor energy productivity of the industrial sector. The results show an upward trend in energy productivity, and the total factor productivity of energy-intensive sectors has grown faster. Regarding the environmental regulation as a quasi-natural experiment, the difference-in-difference-in-difference estimations validate and quantify the promotion effect on energy productivity. Furthermore, the mechanism analysis specifies the transfer channels as energy structure optimization and green technology innovation. China is currently at a critical period of transition to green and clean development. Our findings will provide theoretical support for the mechanism design of environmental and energy policies.

FDI, technological progress, and green total factor energy productivity: evidence from 281 prefecture cities in China

FDI, technological progress, and green total factor energy productivity: evidence from 281 prefecture cities in China

Determinants of firm-level energy productivity – Evidence from the Korean emission trading scheme

With increasing global pressure on transition to carbon neutrality, various technical and policy efforts such as emission trading scheme and carbon tax are being made to improve energy productivity. Yet, there are not many literatures that analyze determinants of firm-level energy productivity, which is an important issue as energy use in industry accounts for one fourth of global carbon emission. This study investigates factors affecting energy productivity such as technology innovation, environmental policy and energy price based on firm-level data from the Korean Emission Trading Scheme (KETS). The total factor energy productivity is estimated by industry based on stochastic frontier analysis (SFA) and panel data analysis is performed to identify determinants of firm-level energy productivity. The results show that energy productivity in Korea has been improved since 2016, and technical progress and environmental policy play an important role in promoting energy productivity. In addition, analysis identifying industry-specific characteristics is performed as their production process and energy consumptions structures vary. The result shows that innovation activity does not significantly affect energy productivity in process industries, but it does in the assembling and high technology industries. This paper implies that policy design reflecting industry-specific characteristics is important to improve energy efficiency more effectively.

The impact of regional market integration and economic opening up on environmental total factor energy productivity in Chinese provinces

China is the world's largest energy consumer; how can Chinese limited energy resources ensure a continuous development of its economy? This paper examines the impact of China's market integration and economic opening up on the Environmental Total Factor Energy Productivity (ETFEP) of 29 provinces in China for the period 1999–2017 and have the following findings: (1) China should strengthen its resource management in improving its ETFEP by providing indispensable impetus for ETFEP to turn from negative to positive growth and, meanwhile, making technological progress to bring about a two-wheel drive mode. (2) The key to improving Chinese ETFEP lies in inland China. The inland region should take over the industries transferred from coastal areas in a scientific manner, and avoid falling into the poverty accumulation trap of “being behind→introducing→lagging behind→introducing again→and falling behind again”. (3) Coastal areas should achieve the goal of increasing its ETFEP by speeding up its market integration, matching the higher economic opening up situation of the coastal areas. The key to improving the ETFEP of the inland areas is to focus on inter-regional market integration and the priority of this integration should be placed over the economic opening up, which seems more realistic at this stage.

The impact of production factor distortion on total factor energy productivity: insight from China's region level.

Clarifying the impact of production factor distortion on total factor energy productivity (TFEP) is a key approach to promote production greenization at the region level. To achieve it, this paper develops a theoretical model to bridge production factor distortion and TFEP with China's four regions as the investigated object based on provincial data from 2003 to 2017. Empirical results show that there exist obvious production factor distortions in China's four regions, and such distortions significantly negatively influence the volatility of TFEP as a whole. Specifically, the allocation of production factors including skilled labor in Eastern and Northeastern China as well as capital input in equipment purchase sector in Central and Western China can significantly improve regional TFEP. What's more, distortions of quite a few production factors including skilled labor in Central and Western China, unskilled labor in four regions, and capital input in some industrial sectors in Central and Northeastern China can all hinder the growth of regional TFEP. Therefore, in order to improve regional TFEP in China and reduce factor distortions, the government need to focus more on the input structure of these production factors that have negative impacts on TFEP, and then promote the institution reform towards the high-quality economy growth mode.

Measuring Energy Efficiency in Kerala: Data Envelopment Analysis

Traditionally, there are two basically reciprocal energy efficiency Indicators: one, in terms of energy intensity, that is, energy use per unit of activity output, and the other, in terms of energy productivity, that is, activity output per unit of energy use. The enquiry that has proceeded from the problems associated with this method of a single energy input factor in terms of productivity has led to multi-factor productivity analysis. We have here two approaches: parametric and non-parametric. Parametric approach famously includes two methods: the erstwhile popular total factor energy productivity analysis and the currently fanciful stochastic frontier production function analysis; The non-parametric approach is popularly represented by data envelopment analysis. The present paper is an attempt to measure efficiency in electrical energy consumption in Kerala, India. We apply the non-parametric mathematical programming method of data envelopment analysis of the multi-factor productivity approach, and estimate the efficiency measures under the two scale assumptions of constant returns to scale (CRS) and variable returns to scale (VRS); the latter includes both increasing (IRS) and decreasing returns to scale (DRS). Scale efficiency measures are also given to find out whether a firm is operating at its optimal size or not, implying degrees of capacity utilization.

Has Disparity Among Regions in China in Environmental Total-Factor Energy Productivity Decreased?

This paper, in the framework of “total factor” and “output diversity”, has measured the environmental total-factor energy productivity (ETFEP) of 29 Chinese provinces/regions for the period between 1999 and 2014, and, by means of structural diverge indicator and exploratory spatial data analysis tool, has analyzed the disparity among regions and spatial effect in environmental total factor energy productivity. The result shows that 1) in the observation period, the ETFEP of China displays an evolutionary characteristics of a “downward-upward-downward” trend; 2) after 1999, although the Chinese government, as a strategy, meant to give rise to a balanced growth among the regions, their differences in ETFEP have not been reduced.

Spatiotemporal Dynamics of Ecological Total-Factor Energy Efficiency and Their Drivers in China at the Prefecture Level.

Improvement of ecological total-factor energy efficiency (ETFEE) is crucial for transformation of China’s economic growth pattern, energy conservation and emissions abatement. Here we combined the epsilon-based measure (EBM) and the Global Malmquist-Luenberger (GML) productivity index to evaluate ETFEE and ecological total-factor energy productivity (ETFEP) and its decompositions for 283 prefecture-level cities in China between 2003 and 2013. A spatial econometric model is used to investigate factors influencing ETFEE and ETFEP. Results indicated that ETFEE, ETFEP and corresponding trends differ significantly depending on whether environmental constraints are considered. No convergence trend was found in ETFEE between prefecture-level cities. Technical progress plays the largest role in increasing ETFEP growth. Pure efficiency change and scale efficiency change, however, are the main hindering factors. Boosting cumulative technological progress, cumulative scale efficiency growth rate and cumulative pure efficiency growth rate are important means of increasing ETFEP. I also found that areas with high levels of economic development do not completely overlap with areas of high ETFEE. Surprisingly, the fiscal expenditure on scientific undertakings and technological spillover effects from foreign direct investment (FDI) have not substantially increased ETFEE. Whereas increased industrialization hinders the improvement of ETFEE. Furthermore, reducing per capita energy consumption help boost ETFEE. In addition, endowment advantages of factors of production have a positive overall effect on improving ETFEE. Lastly, important policy implications are inferred.