RESUMEN

European manufacturing firms have to cope with the new regulations that advocate a greener and more sustainable future with less emissions and at the same time enhance or at least maintain their productivity levels. A unique dataset is constructed by combining information on different firms' pollutants with their financial information during the 2011-2017 period. A non-radial directional distance function analysis is adopted with desirable and undesirable outputs to estimate environmental productivity growth and its components, which addresses the problem of heterogeneity. A regulatory impact indicator that provides information about the loss of outputs resulting from new policies is also computed. Finally, the impact of environmental regulations on productivity growth is explored using a panel vector autoregressive method. Our findings showcase different average values of productivity for each pollutant group. Moreover, results indicate that increasing the index of regulations by 1%, increases environmental productivity by 0.24% and 0.44% for heavy metals and greenhouse gases groups, respectively. Finally, results support the "weak" Porter Hypothesis, which attests that welldesigned environmental regulations can exert a positive effect on environmental innovation.

Asunto(s)

RESUMEN

In the context of accelerated development of the digital economy, whether enterprises can drive green total factor productivity (GTFP) through digital technology has become the key to promoting high-quality development of the economy and achieving the goal of "dual-carbon", However, the relationship between digital transformation and GTFP is still controversial in existing studies. Based on the data of 150 listed companies in China's A-share energy industry from 2011 to 2021, this study empirically analyzes the impact of digital transformation on GTFP using a fixed-effect model. The study shows an inverted U-shaped nonlinear effect of digital transformation on enterprises' GTFP, and the conclusion still holds after a series of robustness tests. Mechanism analysis shows that enterprise investment efficiency and labour allocation efficiency play a significant mediating role in the above inverted U-shaped relationship, in which the inverted U-shaped relationship between digital transformation and GTFP mainly stems from the influence of enterprise investment efficiency. Heterogeneity analysis finds that the inverted U-shaped relationship between digital transformation and GTFP of enterprises is more significant in large-scale enterprises, new energy enterprises and enterprises in central and western regions. The study's findings provide important insights for enterprises to promote digital transformation and realize the green and high-quality development of the energy industry.

RESUMEN

China's transportation sector is a vital link between production and consumption, but it also has issues with low efficiency, high carbon emissions, and technological bottlenecks. To improve efficiency and provide actionable recommendations and strategies, this study first constructs a comprehensive evaluation index system to gauge the transportation sector's inputs using panel data from different Chinese provinces from 2007 to 2021. Within the assessment system, the principal component analysis (PCA) method is used to reduce the dimension of the indexes, thereby yielding a set of adjusted inputs. Subsequently, the transportation system efficiency (TSE) is evaluated using the super-efficiency SBM-DEA model, which includes unexpected outputs such as carbon emissions, and three-stage DEA modifies the efficiency. Then, we calculate the Malmquist-Luenberger index (TML) and its components: technological change (TTC) and technological efficiency change (TEC). Lastly, the influential factors impacting TSE are analyzed via a truncated regression Tobit model. The following are the conclusions: (1) The transportation industry in China exhibits inefficiency, and the average TSE in Stage I and III is 0.91 and 0.93, respectively. TSE is underestimated due to the influence of external environmental factors and inefficiencies in management in Stage I. (2) TSE in the eastern area also produces significant carbon emissions that surpass the national average. At the same time, other regions face efficiency limitations due to geographical constraints and management obstacles. (3) Insufficient technical capacity is a major cause of inefficiency in the transport sector and is prevalent in the northeast, west, and central regions. (4) Population growth and income per capita advancements foster transportation industry development, while increased GDP, fiscal revenues, and traffic accidents contribute to declining efficiency. The study above findings serve as a foundation for regional and national management initiatives and policies to enhance transportation effectiveness.

RESUMEN

Resource constraints and environmental challenges have emerged as serious impediments to the sustainable development of China's broiler industry, with potentially adverse consequences. The pursuit of sustainable development in China's broiler industry is predicated on significant reductions in manure and pollutant emissions from broiler farming. This study utilizes the slacks-based model and the global Malmquist-Luenberger index to calculate the green total factor productivity of broiler breeding across various provinces and scales from 2005 to 2020 within a joint production framework of considering undesirable outputs and desirable outputs. Fluctuations in economic distribution of broiler breeding are characterized using the kernel density estimation, and a convergence analysis is performed via absolute and conditional ß convergence methods. The results revealed an overall upward trend in China's broiler farming green total factor productivity from 2005 to 2020, corresponding to green total factor productivity in small-, medium-, and large-scale broiler breeding were 1.015, 1.017, and 1.009, respectively. The kernel density curve implies a narrowing trend in the discrepancy of green total factor productivity levels among provinces in broiler breeding of varying scales. For all scales, broiler breeding's green total factor productivity demonstrates considerable conditional and absolute ß convergence. Therefore, improving the efficiency of broiler breeding while addressing externalities requires the cultivation of broilers at different scales across diverse regions, coupled with an increased focus on improving the utility efficiency of broiler waste fertilization.

RESUMEN

The paper examines the efficiency and productivity of Sub-Saharan African (SSA) countries towards maternal and infant healthcare services between 2015 and 2019. Data envelopment analysis is utilised to evaluate efficiency, and Malmquist-Luenberger's (ML) productivity estimation is employed for productivity analysis. The results indicate inefficiency in SSA maternal and infant healthcare services. Average efficiency is pegged at 85%, and 60% of the countries evaluated had below-average efficiency. Effects of socioeconomic dynamics of countries were analysed. Preliminary estimations on the impact of Gross domestic product (GDP), education, urban population, and total population on efficiency are not significant. Although GDP and education sometimes show that they influence efficiency positively. Sensitivity analysis indicates efficiency to be more responsive to health expenditure, as well as to nurses and midwives. ML Productivity decomposition into technical efficiency change and technological change indicates improvement in technical efficiency as the principal driver of efficiency and productivity. Policy recommendations are made in line with the findings, requirements, and constraints of SSA countries.

Asunto(s)

RESUMEN

Environmental degradation has attained much attention from researchers and policymakers at national and global levels. The ever-increasing energy use in production methods is considered one of the fundamental reasons for environmental degradation. The concept of environmental efficiency in the wake of sustainable growth evolved in the last three decades. The present study has been designed to estimate environmental efficiency using the Malmquist-Luenberger productivity index (MLI) using annual data from 43 Asian countries from 1990 to 2019. The MLI is an established econometric approach to estimate cases where input variables are used to get output variables in desirable and undesirable forms. Labor, capital, and energy consumption are input variables, while carbon dioxide (CO2) emissions (undesirable variable) and gross domestic product (undesirable variable) are taken as output variables. The results suggested that, on average, environmental efficiency has decreased by 0.3% over the period in selected Asian countries. Cambodia, Turkey, and Nepal have the highest total factor productivity (TFP) output growth rate on average among 43 Asian countries. These countries are excellent examples of sustainable development that balances environmental protection and efficiency. On the other hand, Kuwait, Mongolia, and Yemen showed the least TFP growth. The study also employed unconditional and convergence tests where the countries' conditional convergence is based on foreign direct investment, population density, inflation, industrialization, and globalization. Some policy implications for Asian countries are also discussed at the end of the study.

Asunto(s)

RESUMEN

Promoting greener and sustainable development is one of the main goals of the most recent 14th Five-Year Plan (i.e., 2021-2025). Environmental regulation is seen as fundamental to green transformation and an important way for all of China to reach a high-quality and sustainable development mode. However, large spatial disparities exist across the different regions in China, so formulating region-oriented environmental regulatory policies to achieve regional high-quality and sustainable development is now a matter of great practical significance. In the present paper, we analyze this problem and begin by calculating the high development level measured through the Green Total Factor Productivity (GTFP) of 259 Chinese cities. Thereafter we construct a comprehensive index of environmental regulation through the linear weighted-sum method. Lastly, we investigate the spatiotemporal heterogeneity of the impact of environmental regulation on GTFP using a Geographically and Temporally Weighted Regression (GTWR) model. We find that: (1) From the spatial dimension perspective, the impact of environmental regulation of Chinese cities on GTFP is either linear (monotonically increasing or decreasing), non-linear (U-shaped or inverted U-shaped), or nonsignificant. Most cities have a U-shaped relationship, indicating that environmental regulation first inhibits GTFP at the early stage, but then promotes it. There are also significant differences among cities in the turning points of environmental regulation; (2) From the time dimension perspective, the number of cities is on the rise having monotonically decreasing impacts of environmental regulation on GTFP. Furthermore, even for the same city, the relationship between the two variables shows different characteristics in different years; (3) The impact of five control variables on GTFP may also vary from one city to another over the sample period, also presenting spatiotemporal heterogeneity effects. Consequently, the formulation and implementation of environmental regulatory policies should not only adapt to local conditions but also choose reasonable and effective measures to achieve high-quality development targets.

Asunto(s)

RESUMEN

To measure and analyze the evolution characteristics and influencing factors of China's industrial green development level is of great significance in achieving carbon neutrality goal. Based on the panel data from 2000 to 2018 of 30 provinces in China, this research uses the super slack-based measuring model and the Malmquist-Luenberger index to calculates China's industrial green total factor productivity and to describe its evolution characteristics using the kernel density function and moreover uses the Spatial Durbin model and the partial differential method to explores its main influencing factors. This study finds that China's overall industrial green development level is not high but shows an upward trend year by year; carbon emissions, fiscal decentralization, and urbanization are not conducive to improving the national industrial green development level, whereas economic development, foreign direct investment, industrial structure, and technological progress are positive contributors. At the same time, the level of economic development and technological progress have significant direct and spatial spillover effect. Our findings also provide some policy implications for improving China's industrial green development.

Asunto(s)

RESUMEN

It is vital to determine the changing spatiotemporal patterns and driving factors of green total factor energy efficiency (GTFEE) in order to design scientific policies to promote energy efficiency in the Regional Comprehensive Economic Partnership (RCEP) region. From 2010 to 2019, the super-efficient SBM model and the global Malmquist-Luenberger index provide an appropriate framework for measuring the spatiotemporal evolution of GTFEE and the dynamics of energy productivity in RCEP countries. With the coefficient of variation and the Thiel index, an extensive view of the spatiotemporal variance in GTFEE is offered, taking regional heterogeneity into account. Furthermore, the Tobit model is introduced to investigate the factors influencing the GTFEE of RCEP members, which may address the restricted values of the dependent variable when compared to the least squares regression model. Findings suggest that (1) The GTFEE of RCEP members tends to be low and unevenly distributed spatially and temporally, with much room for improvement. (2) The energy productivity index fluctuates strongly, and the improvement primarily comes from technological progress. (3) The Non-ASEAN region possesses higher GTFEE than the ASEAN region, albeit regional variations are diminishing. (4) In terms of the major factors influencing the regional GTFEE, the non-ASEAN region looks to be distinct from the ASEAN region. The findings shed light on the trends and influencing factors of GTFEE in RCEP and serve as a resource for international energy cooperation and sustainable development.

Asunto(s)

RESUMEN

In order to systematically understand the evolution of total factor carbon productivity and explore its influence mechanism, based on panel data of 30 Chinese provinces from 2005 to 2019, the slacks-based measure of directional distance functions model and the Luenberger index are used to estimate the evolution of total factor carbon productivity, and the SYS-GMM model is constructed to explore the drivers of total factor carbon productivity and its influence effect. The results show that from 2005 to 2019, the overall level of total factor carbon productivity was low, but its growth index and decomposition term both showed an increasing trend; the development of total factor carbon productivity has regional differences. Only the eastern, northern, and middle Yellow River economic regions experience positive growth in total factor carbon production. The downward trend of total factor carbon productivity is most significant in the northwest and southwest economic regions, with - 2.577% and - 1.463%, respectively; improvements in scale technology are the main reasons for improving total factor carbon productivity across time and regions; economic growth and environmental regulations contribute to total factor carbon productivity at 1% significance level, and industrial structure has a negative impact. Foreign direct investment inhibits total factor carbon productivity, but the effect is not significant. Based on these findings, this paper provides an effective reference for achieving the goal of low-carbon sustainable development and improving total factor carbon productivity.

Asunto(s)

RESUMEN

China's energy-intensive industry (EII) is characterized by high pollution, high energy consumption, and high emissions. It is essential to boost this sector's green total factor productivity (GTFP) in order to support the sustainable development of the China's economy and help to achieve the objective of carbon neutrality. This work measures the evolution of GTFP in EII and its subsectors at provincial and regional level from 2001 to 2019, identifies the causes of these changes, and finally analyzes the particular spatial aggregation effect of GTFP in EII. It is discovered that the GTFP of China's EII has significantly improved throughout the sample period and exhibits a spatial structure of "high in the coastal areas and low in the west and center." The main driver of GTFP growth for China's EII and its subsectors was technological advance. Smelting and pressing of ferrous metals (SPFM) and smelting and pressing of non-ferrous metals (SPNM) were the industries with the most significant technological progress. Remarkable spatial correlations existed among the GTFP of EII at provincial level. The GTFP values of EII in coastal regions were relatively high and tend to benefit the adjacent provinces but there was a polarization effect in the Middle Reaches of Yellow River (YR). Finally, policy implications are provided for the sustainable development of China's EII.

Asunto(s)

RESUMEN

Improving carbon emission efficiency (CEE) has emerged as a critical way for Regional Comprehensive Economic Partnership (RCEP) members to promote carbon reduction in the context of climate change mitigation and carbon neutrality. The super-efficiency slacks-based measure (SBM) model, which considers non-desired outputs, is adopted to comprehensively assess the current state and trend of CEE in 15 RCEP countries from a spatio-temporal dynamic perspective, and the global Malmquist-Luenberger (GML) index is coupled to quantify the spatial and temporal differences and dynamic changes. Following that, taking into account the spatial characteristics of CEE, the extended STIRPAT model and the spatial Durbin model are combined to further investigate the primary influencing factors of CEE. It is found that (1) the CEE of RCEP members is generally poor and unevenly distributed in temporal and spatial dimensions, with significant room for improvement and an overall positive spatial autocorrelation; (2) CEE varies considerably among RCEP members, with developed countries far outstripping developing countries in terms of both the current status and trend of CEE; (3) on a dynamic level, the GML index exhibits W-shaped fluctuations, with technological progress acting as the dominant force; and (4) in terms of spillover effects, affluence and economic agglomeration inhibit CEE enhancement, whereas technology level and investment capacity facilitate it. The findings will be useful in developing carbon-neutral plans for various countries as well as coordinated sustainable development for RCEP regions.

Asunto(s)

RESUMEN

Regional carbon emission efficiency (CEE) has differentiated characteristics under different economic development stages and patterns, and identifying such characteristics is important for formulating corresponding policies for high-quality regional development. Using input‒output data related to economic development and energy consumption, a comprehensive evaluation model of the Super-SBM and Malmquist‒Luenberger (ML) index is constructed to evaluate the spatial and temporal changes and driving forces of CEE. Based on this index, a proposal is designed for collaborative carbon emission reduction zoning. The results indicate that the CEE of the Yangtze River Delta shows a fluctuating upward trend with obvious spatial agglomeration characteristics, and CEE changes are closely related to economic development stages. The annual average CEE values in each stage show positive changes, indicating that economic development gradually evolves to low carbonization levels. Moreover, CEE improvement gradually shifts from being driven by efficiency changes to being driven by technological changes. Finally, according to the characteristics of total carbon emissions and the efficiency of different cities, a synergistic emission reduction path is proposed with four aspects: land use optimization, ecological co-preservation, innovation cooperation and low carbon development.

Asunto(s)

RESUMEN

The continued expansion of agriculture must contend with the dual pressures of changing factor endowment structure and constrained resources and environments. The main purpose of this paper is to provide feasible ideas for high-quality agricultural development in the transition period through the research on the green-biased technical change in Chinese agriculture. This paper selects China's provincial panel data of the agriculture industry from 1997 to 2017, combining the DEA-SBM model and Malmquist-Luenberger index decomposition method to calculate the green-biased technical change (BTC) index; second, the influence mechanism of BTC is empirically investigated by using the panel data regression analysis approach. The results show that: (1) in China's agriculture industry, BTC is the driving force behind long-term and steady improvement of technological advancement. Specifically, input-biased technical change (IBTC) has a substantial enhancing effect on agricultural green total factor productivity (GTFP), whereas output-biased technical change (OBTC) has a certain inhibiting effect. (2) On the whole, the tendency of capital substituting for labor and land is very evident, whereas the biased advantage of desirable output is not particularly prominent. (3) The BTC index in Chinese agriculture varies regionally. The eastern region has the highest IBTC index but the lowest OBTC index. (4) The degree of marketization, urbanization, capital deepening, financial support for agriculture, and other factors have a promoting effect on IBTC, whereas most of them have a restraining effect on OBTC. There is evident regional heterogeneity in the effect of environmental regulation intensity on BTC. The following are the primary contributions of this paper: based on national conditions in China, this paper empirically explores the changes and internal rules of green-biased technical change in China's agriculture industry from various regional viewpoints. It provides an empirical foundation for the regional diversification of agricultural green transformation.

Asunto(s)

RESUMEN

Clarifying the efficiency of investment in environmental pollution control is conducive to better control of environmental pollution. Based on panel data of 30 provinces and cities in China from 2008 to 2017, this study combines the three-stage super-efficient SBM-DEA model and the Global-Malmquist-Luenberger index to measure the efficiency of investment in environmental pollution control in China and analyze regional differences. The results show that: First, the investment efficiency of environmental pollution control in China shows a rising trend year by year, but there are significant differences among provinces and regions; the presence of random factors and environmental variables makes the control efficiency underestimated. Second, excluding the effects of both, the national investment efficiency of environmental pollution control has improved significantly, but still has not reached the optimal effect; the gap between provinces and regions has narrowed while the investment efficiency of environmental pollution control has improved, and there is still an unbalanced situation. Third, the main driver of the year-on-year improvement in China's environmental pollution control efficiency is technological progress; compared with northeastern China, technological progress has a more significant role in promoting eastern, central, and western China. Finally, based on the results, this paper focuses on making suggestions to promote environmental pollution control in China in terms of making regional cooperation, making good environmental protection investment and strengthening environmental protection technology research and development.

Asunto(s)

RESUMEN

Improving green energy efficiency is crucial to promoting China's high-quality economic development and reducing the environmental pollution. In this paper, the Malmquist-Luenberger index measures the green energy efficiency of 30 provinces in China from 2004 to 2019. Based on the measurement results, the spatial Durbin model is used to empirically study the impact of environmental regulation on green energy efficiency and its spatial spillover effects. The results show that China's green energy efficiency is low, and its growth mainly comes from technological progress (TECH) rather than technological efficiency (EFFCH). The eastern region has the highest efficiency of green energy, followed by the West and the lowest in the Central region. The estimation results of the spatial Durbin model show that both environmental regulation and green energy efficiency have a significant spatial correlation. Environmental regulation can improve the green energy efficiency in the local province but inhibit green energy efficiency in the adjacent provinces. Finally, this paper puts forward relevant policy suggestions according to the research results.

Asunto(s)

RESUMEN

China's rapid economic growth has caused serious problems, such as environmental pollution and resource exhaustion. Only by improving the green total factor productivity (GTFP) can China's economic development get out of the dual dilemmas of environmental degradation and resources exhaustion. Although environmental regulation helps to improve China's productivity, its impact on GTFP is still controversial and deserves careful investigation. In this context, this study adopts the global Malmquist-Luenberger productivity index to measure the GTFP change of China's 30 provinces over the period of 2003 to 2017 and then it uses the fixed-effect dynamic panel model to investigate the impact of environmental regulation on GTFP from the perspective of governance transformation. The results show that: (1) there is a nonlinear U-shaped relationship between environmental regulation and GTFP, indicating that the Porter hypothesis is verified in China. More notably, the values of environmental regulation are still located on the left side of the U-shaped curve at present, which means that the promotional effect of environmental regulation on GTFP has not been realized fully. (2) The U-shaped relationship shows significant regional heterogeneity. The western region demonstrates the highest level of significance, followed by the eastern region. However, the U-shaped relationship is insignificant in the central region. (3) Governance transformation can not only significantly improve GTFP but it can also accelerate the realization of the Porter hypothesis by inspiring the innovative enthusiasm of enterprises, which means that governance transformation can contribute to the achievement of the improved effects of environmental regulation on GTFP. (4) R&D investment can significantly improve GTFP, where the impacts of trade openness and factor endowment were significantly negative and the influence of foreign direct investment was not significant. These conclusions provide a good reference point for optimizing the relationship between the government and the market, as well as promoting regional green and high-quality development in China.

Asunto(s)

RESUMEN

The environmental problems caused by industrial development in the Yangtze River Economic Belt in China have become a bottleneck for urban economic development. Therefore, the measurement of the green efficiency of the urban industry in the Yangtze River Economic Belt can provide a reasonable basis for future industrial green development paths. Based on the DEA model, this study measures the industrial green total factor productivity of 108 cities in the YREB by combining the meta-frontier infrastructure framework and the Malmquist-Luenberger index. The results indicate the following: (1) The industrial green total factor productivity of cities in the Yangtze River Economic Belt declined at first and then increased from 2009 to 2016. The overall performance indicated that the structures of the technological efficiency change index and technological change index were unreasonable. The industrial green total factor productivity of midstream cities is the highest, and the difference between cities is influenced by economic development level, industrial base, transportation convenience, labor quality, and the government's emphasis on green development of industrialization. (2) Different regions have different decomposition efficiency to promote the improvement of ITFP. Considering regional heterogeneity, the industrial green total factor productivity in the middle and lower reaches of the region relies on the advantages of technical efficiency, whereas the upper reaches rely on the improvement of management efficiency. (3) The technology gap in the upper and lower reaches is small, and in the middle reaches is relatively large. Thus, according to the above empirical results, this study finally presents some policy suggestions for industrial green development in different cities of the Yangtze River Economic Belt.

Asunto(s)

RESUMEN

The Yellow River basin (YRB) is China's most critical energy consumption and coal production area. The improvement of carbon emission reduction efficiency in this area is the key for the Chinese government to achieve the 2030 carbon peak and 2060 carbon neutral ("30.60"). Given this, this study first calculates the carbon emission efficiency of YRB from 2005 to 2019 based on the slack-based measured directional distance function (SBM-DDF) model and combined with Malmquist-Luenberger (ML) index and decomposes the carbon emission efficiency of each province. Then, a panel Tobit model with random effect is constructed to measure the influencing factors and their influence degree of carbon emission efficiency of YRB. Finally, the main influencing factors are selected, and policy suggestions on how to improve the carbon emission efficiency of each province are put forward with the help of the coupling coordination degree (CCD) model. The results show that first, the carbon emission efficiency of each province is significantly different, but it shows a fluctuating upward trend on the whole. Second, the reasons for the rise or decline of the ML index in different provinces are different. Therefore, the development strategies of different provinces should be formulated from the perspective of accelerating technological progress and improving technical efficiency. Finally, the calculation results of influencing factors and coupling coordination degrees show that provinces with high coupling coordination degrees should focus on developing per capita power consumption and controlling per capita power consumption to consolidate the actual urbanization process and industrial structure adjustment. Provinces with low coupling coordination degrees should focus on maintaining the urbanization process and increasing the development of the tertiary industry. Therefore, to fundamentally reduce carbon emissions in YRB areas, we need to consider implementing differentiated emission reduction schemes based on national strategic objectives and in combination with the development characteristics of various provinces.

Asunto(s)

RESUMEN

An improvement in total factor productivity of the power industry (TFPPI) is crucial for achieving China's green transformation and high-quality development. Considering the technological heterogeneity of different regions, the Meta-frontier Global Malmquist-Luenberger(MGML) index was constructed to measure TFPPI in China's 30 provinces from 2003 to 2017, and then we investigated the dynamic evolution and regional differences of TFPPI. On this basis, the two-step system GMM method was used to explore the influencing factors of TFPPI. The results showed that: (1) During the survey period, the average annual growth rate of China's TFPPI was 4.2%, and average values of TFPPI in all provinces were greater than 1. The innovation effect was the key to TFPPI growth, while the catch-up effect and leading effect were not significant. (2) TFPPI showed a decreasing trend from the western to eastern and central regions, with average annual growth rates of 5.69%, 3.66%, and 2.89%, respectively, and the driving factors of each region were different. Moreover, the technology gap was narrowing. (3) Both the economic development level and R&D level had a positive impact on TFPPI, whereas the power consumption intensity was negatively correlated with TFPPI. Based on the conclusions of this paper, some effective recommendations were put forward to improve TFPPI.

Asunto(s)