Slacks-based Model Research Articles

Liability accounting of natural resource assets from the perspective of input Slack—An analysis based on the energy resource in 282 prefecture-level cities in China

Natural resource assets-liabilities accounting is an essential theoretical and institutional innovation in constructing China's ecological civilization. As the primary account of the natural resources balance sheet, energy resources have typicality and representativeness. Unlike previous research focused on theoretical and practical research, this paper fully considers energy resources' exhaustibility and ecological value, and it organically combines the input slack based on the total factor productivity framework with the accounting of energy resources assets-liabilities. Employing the slack-based model with undesirable output and collecting samples including 282 prefecture-level cities in China from 2000 to 2019, this paper analyzes the energy resources assets-liabilities characteristics and evolution mechanism. The results show that the slack-based model with undesirable output effectively measures the excessive input of energy resources and the current energy resources assets-liabilities caused by backward utilization technology and configuration management. From 2006 to 2019, the average assets-liabilities of energy resources in China's prefecture-level cities show a growing trend and club convergence. Finally, the energy resources assets-liabilities in the initial, comprehensive, and post-industrialization economic development stages have significant differences and roughly conform to a U-shape. The conclusions obtained in this paper provide cases for the preparation and research of the natural resources assets-liabilities accounting and contribute to the rational utilization and scientific management of China's natural resources.

A Study of the Spatial Structure and Regional Interaction of Agricultural Green Total Factor Productivity in China Based on SNA and VAR Methods

As regional interaction increases in an open economy, a region’s green total factor productivity in agriculture must be considered alongside relationships with other regions. In this study, the slack-based model (SBM) global Malmquist–Luenberger (GML) index is used to measure the green total factor productivity of agriculture in each province of China, and the social network analysis (SNA) and vector autoregressive model (VAR) impulse response function (IRF) are used to examine the spatial network structure and regional interactivity. The research confirms that the absolute value and concentration of agricultural green total factor productivity are generally higher in the south than in the north of China, but the peak is lower in the south than in the north. The network density of agricultural green total factor productivity in China from 2008 to 2019 shows an increase, with the cut-off values of mean, 10, 50, and 100 treated as 4.97%, 2.57%, 3.30%, and 2.43%, respectively. From 2008 to 2019, the central potentials of network entry and network exit of green total factor productivity in China’s agriculture show a “V”-shaped and inverted “V”-shaped evolution path, respectively, with the density of cohesive subgroups growing, which demonstrates that the spatial structure of green total factor productivity in Chinese agriculture has experienced an evolutionary path from polycentric to monocentric to polycentric conditions. The spatial interaction of different cohesive subgroups is intensifying and has a certain degree of self-stability. In terms of regional interaction, the siphon effect of the east on the green development of agriculture in the central and western regions is significant, but the trickle-down effect is not obvious, and the interaction between the central and western regions has a catalytic effect on the efficiency of the green economy of agriculture in both regions. It is recommended that targeted policies be introduced to support the flow of agricultural factors and industrial division of labour between the central and western regions and the south and north, taking into account the actual situation. The novelty of this paper is that it focuses on the green total factor productivity of Chinese agriculture and combines the innovative use of the social network analysis paradigm to analyse the green development of agriculture in a country from a spatial dynamic evolutionary perspective. A limitation of the research methodology in this paper is its poor applicability to closed economy analysis.

Does Rapid Urbanization Improve Green Water-Use Efficiency? Based on the Investigation of Guangdong Province, China

The green water-use efficiency considers the undesired output of water pollution discharge as well as desirable output, pursuing the unification of economic and ecological benefit. In recent years, China has been undergoing a process of rapid urbanization and a dramatic change in the water utilization pattern, while the water pollution discharge is becoming more obvious. Taking Guangdong Province as an example, the largest economy and urbanization pilot area, exploring the impact of different aspects of rapid urbanization on green water-use efficiency is of great significance for promoting new-type urbanization and green development. This study adopts the super efficiency slack-based model (SE-SBM) considering undesirable output to measure the green water-use efficiency of each city in Guangdong Province. On this basis, mixed panel, random panel, and fixed panel models are used to explore the relationship between industrial urbanization, population urbanization, land urbanization, social urbanization, and urban-rural integration on green water-use efficiency. The results reveal that Guangdong’s green water-use efficiency follows a low upward trend. The green water-use efficiency of the Pearl River Delta has improved rapidly, while other regions are maintained at a low level, so regional disparities are also widening. Industrial urbanization and land urbanization have a significant positive effect on green water-use efficiency, while social urbanization and urban-rural integration have a significant negative effect. The effect of population urbanization on the efficiency is not significant. The construction of new-type urbanization should be accelerated, the green transformation and upgrading of industries should be guided, and the urbanization of agricultural migrants should be promoted. It is necessary to rationally plan and utilize urban land resources and comprehensively improve urban functions and livability.

Growth Targets Management, Regional Competition and Urban Land Green Use Efficiency According to Evidence from China.

Based on the panel data of 257 prefecture-level cities in China from 2010 to 2017, this paper measured urban land green use efficiency (ULGUE), incorporating undesirable outputs, via the super efficiency slack-based model (SBM). It also explored the effect, mechanism, and heterogeneity of growth targets management and regional competition on ULGUE via the time-varying gravitational spatial weight matrix and the spatial self-lagging model. The results show that growth targets management and regional competition have significant positive effects on ULGUE, and enhance the ULGUE by promoting local investment attraction, increasing innovation inputs, optimizing environmental regulations and strengthening commercial activities. Additionally, growth targets management has a more significant effect on eastern cities, non-central cities, and mature urban agglomeration, while regional competition has a more significant effect on central cities, non-central cities, and developmental urban agglomeration. Therefore, considering development as the priority, setting relatively aggressive economic growth targets and optimizing the regional competition mechanism for growth targets management can help improve the ULGUE and promote high-quality economic development in China.

Can green finance improve carbon emission efficiency?

This article discusses the influence and mechanism of green finance on carbon emission efficiency. Based on the panel data of 27 provinces and municipality in China from 2008 to 2017, the slack-based model of unexpected output is used to measure the efficiency of carbon emissions. On this basis, the Tobit model is used to empirically study the impact and mechanism of green finance on the efficiency of carbon emissions. The consequences exhibit that (1) China's carbon emission efficiency is not high and generally presents a gradient decreasing characteristic of east, middle, and west. (2) Overall, green finance plays a considerable role in improving carbon emission efficiency; by region and group, there are significant differences in the influence of green finance on carbon emission efficiency. (3) The study found that green finance promotes the efficiency of carbon emission through technological progress and industrial structure upgrading. This study provides empirical evidence and policy enlightenment for the realization of carbon peaking and carbon neutrality goals and the evolution of green finance.

Sustainability and profitability efficiencies: the moderating role of corporate social responsibility

AbstractTo develop a better way of assessing the sustainability efficiency (SE) and profitability efficiency (PE) of mining multinational enterprises (MNEs), the two‐stage data envelopment analysis (DEA) network model is proposed. The study shows that slack‐based models (SBM) can strengthen the previous and present measurements of the mining industry. When we assess the SE and PE of 53 mining MNEs from 2016 to 2019, the overall efficiencies of both the sustainability and profitability of mining MNEs are not higher than 70%. Such results are shown to be helpful for suggesting that global mining companies need to improve their efficiencies. This study addresses filling the research gap in the literature review about SE positively and significantly influencing PE with the moderating effect of corporate social responsibility (CSR) in mining MNEs. The result reveals that higher sustainability yields higher returns to the companies. In other words, MNEs have improved their SE to achieve a higher PE. Furthermore, the value of SE significantly influences PE regardless of whether the moderating effect, CSR, is higher or lower. The results show that a higher governance pillar score (GPS) and social pillar score (SPS) lead them to have greater profitability. However, a higher Environmental Pillar Score (EPS) tends to create a weaker relationship between SE and PE. We addressed a few interpretations of why higher EPS negatively influences profitability. Nevertheless, our study strongly recommends companies to increase their CSR score. Last, this study proves that all hypotheses are supported.

Logistics Efficiency under Carbon Constraints Based on a Super SBM Model with Undesirable Output: Empirical Evidence from China’s Logistics Industry

As world resources and environmental constraints have increased, environmental cost has become a concern that affects the sustainable development of the logistics industry in various countries. Carbon emissions are an important part of any environmental cost assessment. How to scientifically and rationally evaluate the green GDP impact and regional efficiency in the logistics industry, especially when under carbon emission constraints, is of great significance to the realization of green and sustainable development. This study evaluated the logistics efficiency of 30 provinces in China from 2003 to 2016 by constructing a super SBM (Slack Based Model) model with undesirable output to explore provincial efficiency and its regional differences. The input–output ratio of the regional logistics industry was optimized through the calculation of the frontier slack variables. The research results showed that, first, it was more reasonable to adjust efficiency under carbon constraints, and it was consistent with the actual performance of the logistics industry. Second, technological progress and deeper capital investments promoted the development of the logistics industry, but technological barriers and low-scale efficiency between regions often limited technological efficiency. Therefore, decision-makers in the logistics industry should reconsider the challenges presented in each reason, encourage industrial technological innovation between regions, and especially promote energy-saving and emission-reduction technologies, so as to maintain the sustainable growth of the logistics industry.

Quantifying the spatiotemporal characteristics of China's energy efficiency and its driving factors: A Super-RSBM and Geodetector analysis

Improving energy efficiency is a key component of ensuring energy security and realizing “dual carbon” goals for China. This study introduced a ray slack-based model (RSBM) and Super-RSBM, which may distinguish weak and strong disposability to provide a more comprehensive and reliable insight compared with traditional models, to measure China's energy efficiency during 2000–2017. Its spatiotemporal characteristics were outlined elaborately by spatial autocorrelation models and Theil index, and its driving factors were probed by Geodetector model instead of econometric models to minimize fitting bias. The results showed that China's energy efficiency remained a medium level over time, and experienced a slightly declining first and then fluctuating. Meanwhile, it maintained a distribution pattern of higher energy efficiency in the eastern region as well as Mongolian and Tibetan Plateau, while lower in the western region and most of North China Plain. It had great inequality across provinces and regions. Specifically, the number of LL-type provinces decreased while provinces of HH-type were always rare. The national inequality of energy efficiency was mainly derived from the intraregional difference. Furthermore, China's energy efficiency was tightly associated with the economic development and urbanization level. It showed a loose connection with environmental regulation and clean production proportion. All pairs of studied factors had mutually reinforcing effects on energy efficiency. These results could serve as strong scientific supports to formulate policies targeted towards energy conservation and emission reduction.

The sustainability of industrial structure on green eco-efficiency in the Yellow River Basin

China attaches great importance to the green eco-efficiency and sustainability of the Yellow River Basin (YRB), but the level and influencing factors of such eco-efficiency there lack in-depth analysis. Thus, this research uses the Slack Based Model (SBM) to calculate the green eco-efficiency of YRB, uses the overall feasible generalized least square method (FGLS) to estimate the regression model, and employs data of 43 cities from 2007 to 2018 to analyze the sustainability of industrial structure upgrading. The results are as follows. First, the overall level of YRB’s green eco-efficiency is low. Second, spatial distribution presents the heterogeneity characteristics of the “middle reaches a depression”, displaying a fluctuating trend after the first decline and then a rise together with changes in time. Third, due to the influence of spatial heterogeneity, the levels of sustainability of industrial structure optimization on green eco-efficiency of the reaches of YRB vary.

SME efficiency in transforming regional business research and innovation investments into innovative sales output

ABSTRACT Based on data provided by the Regional Innovation Scoreboard on 23 capital and 184 non-capital regions in Europe, slacks-based models of data envelopment analysis (DEA) reveal that the efficiency by which business research and innovation inputs are converted at regional-level aggregated innovative sales output in small and medium-sized enterprises was significantly lower in capital regions in the period 2006–14. In view of efficiency maximization, a majority of the capital regions overinvest in non-research and development innovation activities, are over-specialized in knowledge-intensive industries, and fall behind in converting research and innovation inputs in intermediary intellectual property outcomes.

Spatial Distribution and Factors Influencing Ecological Efficiency of the Yellow River Basin in China

Improving urban ecological efficiency is an integral part of ecological protection and high-quality development of the Yellow River Basin. We used the super-efficiency slacks-based model with unexpected output to measure the ecological efficiency of 62 prefecture-level cities in the Yellow River Basin from 2005 to 2018. Its spatial distribution characteristics and convergence are discussed. The influencing factors and spatial spillovers of the ecological efficiency are assessed through the spatial Dubin model. The results show that: 1) The ecological efficiency is highest in cities in the lower reaches of the Yellow River Basin, mid-level in the upper reaches, and lowest in the middle reaches. 2) There is no σ convergence in the ecological efficiency of the Yellow River Basin, but there is absolute β convergence and conditional β convergence. 3) Economic development, financial development, and technological innovation significantly promote ecological efficiency, and the spatial spillover effects are significant. Increased connection with the outside world can significantly improve ecological efficiency, but there is no apparent spatial spillover effect for these connections. The industrial structure and urbanization rate reduced ecological efficiency and have a significant negative spillover effect. The energy consumption structure reduces the level of ecological efficiency, and the spillover effect is not significant.

Spatial-Temporal Evolution and Influencing Factors of Urban Green and Smart Development Level in China: Evidence from 232 Prefecture-Level Cities.

Green and smart city is an optimal choice for cities to realize their modernization of governance capacity and sustainable development. As such, it is necessary to clarify the evolutionary characteristics and driving mechanism of urban green and smart development level (GSDL) systematically. From the perspective of green total factor productivity (GTFP), this study adopted the SBM-GML (slack-based model & global Malmquist–Luenberger) method to measure the urban GSDL considering smart input-output elements. Based on the panel data of China’s 232 prefecture-level cities from 2005 to 2018, the spatial and temporal evolution characteristics of urban GSDL were explored, and the factors and structural mutation points affecting urban GSDL were analyzed with quantile regression tests and threshold regression tests. The findings of this paper showed that (1) there is an upward trend in the volatility of urban GSDL from 2005 to 2018, in which the eastern region was highest, followed by the central and western regions, and the differentiation showed no converge among regions; (2) the effect of technical progress and technical efficiency improvement on the urban GSDL was demonstrated with a fluctuating “Two-Wheel-Drive” trend on the whole; (3) the urban GSDL was promoted by the opening-up level and urban scale significantly, while inhibited by the level of economic development and government size. Additionally, the effects of industrial structure, financial development level, and human capital level on the urban GSDL were distinctive at different loci; (4) the threshold effects of economic and financial development level on improving the positive effects of industrial structure and opening-up level on urban GSDL were significant. These findings may enrich the research literature on the evolutionary heterogeneity of green and smart cities and provide theoretical and practical exploration for the construction of green and smart cities.

Evaluating the effect of urbanization and foreign direct investment on water use efficiency in West Africa: application of the dynamic slacks-based model and the common correlated effects mean group estimator

Evaluating the effect of urbanization and foreign direct investment on water use efficiency in West Africa: application of the dynamic slacks-based model and the common correlated effects mean group estimator

Using Systems Thinking and Modelling: Ecological Land Utilisation Efficiency in the Yangtze Delta in China

Ecological land has two definitions: the broad category and the narrow category. This paper studied the narrow category, which refers to the land whose main purpose is to provide ecological services, such as environmental regulation and biological conservation. Ecological land areas are likely to decrease with the development of urbanisation all over the world, which poses potential risks to the environment, society, and public health, and low utilisation efficiency of ecological land will exacerbate this risk. Based on the actual input–output situation of ecological land utilisation, we constructed an index system in the Yangtze Delta to measure the ecological land utilisation efficiency with a Slack-Based Model (SBM) in the data envelopment analysis (DEA). The results revealed that the comprehensive efficiency exhibited an increasing trend from 2012 to 2020, and more high-efficiency cities appeared in the areas closer to Shanghai. Both the technical efficiency and scale efficiency increased over the research period. The technical efficiencies in most cities were efficient and equal to the scale efficiencies. Among the remaining cities, the technical efficiencies and scale efficiencies were inefficient and not synchronous. In addition, we found that the that poor efficiencies of some cities can mainly be attributed to low-technology, an unsuited-scale, or a combination of these. In the future, improvement should be focused on the economical and intensive use of ecological land, technical innovations, and the optimal scale. The present results can provide references for making scientific decisions regarding the efficient utilisation of ecological land in the Yangtze Delta and other similar rapid-urbanization areas.

Changes in carbon emission performance of energy-intensive industries in China.

As the major energy consumers, energy-intensive industries are the key players in achieving carbon emission reduction targets. The paper builds a super slack-based model (SBM) considering this undesirable output and calculates the carbon emission efficiency. Then, the meta-frontier Malmquist-Luenberger productivity index (MF-MLPI) is constructed to dynamically analyze the growth rate changes of the carbon emission efficiency and the regional differences in energy-intensive industries. Furthermore, the carbon emission reduction potential of the energy-intensive industries in various economic regions of China is discussed, and the conclusions are as follows: there is a big difference in the carbon emission technology gap ratios (TGRs) of the energy-intensive industries in different economic regions; the growth rate of the carbon emission efficiency of energy-intensive industries shows a trend of first declining and then slowly recovering, while the carbon reduction potential generally shows a trend of decreasing and then rising; and the carbon emission reduction potential in the eastern region keeps decreasing.

Estimation of China’s Green Investment Efficiency in Belt and Road Countries -Based on SBM-Undesirable Model and Malmquist Index Model

Green and low-carbon development is an important element of the Belt and Road Initiative, and a reasonable and objective evaluation of China’s green investment efficiency in B&R countries is of great significance to promote the sustainable development of those area. This paper applies the Slack-based measure model that considers undesirable outputs and Malmquist total factor productivity index to measure the efficiency of China’s green investment in B&R countries from 2011 to 2018 from both static and dynamic perspectives, as well as provides an in-depth analysis of the differences, changes, and influential factors. The empirical results reveal that the overall efficiency of China’s green investment in B&R countries is relatively low, showing a distinctly uneven trend and the main driving force of the decline in total factor productivity comes from technical change. Some significant differences have also been reported amongst these countries in terms of their industrial development and income levels. These findings provide a valuable reference for B&R countries to identify unique strategies that can promote their green total productivity factor.

Agricultural Water Use Efficiency: Is There Any Spatial Correlation between Different Regions?

Affected by global climate change and water shortages, food security continues to be challenged. Improving agricultural water use efficiency is essential to guarantee food security. China has been suffering from water scarcity for a long time, and insufficient water supply in the agricultural sector has seriously threatened regional food security and sustainable development. This study adopted the super-efficiency slack-based model (SBM) to measure the provincial agricultural water use efficiency (AWUE). Then, we applied the vector autoregression (VAR) Granger causality test and social network analysis (SNA) method to explore the spatial correlation of AWUE between different provinces and reveal the interprovincial transmission mechanism of spillover effects in AWUE. The results show the following: (1) In China, the provincial AWUE was significantly enhanced, and the gaps in provincial AWUE have widened in the past 20 years. (2) There were apparent spatial heterogeneity and correlations of provincial AWUE. The provinces with higher AWUE were mainly located in economically developed and coastal areas. (3) The correlation of AWUE between provinces showed significant network structure characteristics. Fujian, Hebei, Jiangsu, Shandong, and Hubei Qinghai were central to the network, with high centrality. (4) The AWUE spatial correlation network could be divided into four blocks. Each block played a different role in the cross-provincial transmission of spillover effects. Therefore, it is necessary to manage the agricultural water resources and improve water use efficiency from the perspective of the network.

旅游产业集聚对旅游业碳排放效率的空间溢出效应

PDF HTML阅读 XML下载 导出引用 引用提醒 旅游产业集聚对旅游业碳排放效率的空间溢出效应 DOI: 10.5846/stxb202105091211 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家社会科学基金（18BJY191）；湖南省国内一流培育学科建设项目（5010002） Spatial spillovers of tourism agglomeration on the carbon emission efficiency of tourism industry Author: Affiliation: Fund Project: National Social Science Foundation of China (No.18BJY191); Construction Program for First Class Disciplines of Hunan Province, China (No.5010002) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:在碳中和、碳达峰的时代背景下，提高旅游业碳排放效率对实现旅游产业高质量发展具有重要的实践价值。基于2001-2018年中国省际（自治区、直辖市）面板数据，首先利用区位熵和Super-SBM模型分别测算中国省际旅游产业集聚水平和旅游业碳排放效率，并探究二者空间演变趋势和关联特征；其次，运用空间杜宾模型分析旅游产业集聚对旅游业碳排放效率的影响及其空间溢出效应。结果表明：（1）研究期内，中西部地区的旅游产业集聚水平明显提高，东部地区则无明显变化；除河北省、山西省、内蒙古自治区等地区外，其他省份的旅游业碳排放效率均无显著变化。整体上看，二者高水平地区的空间分布变化大致均呈现出以现有集聚区为中心向周边扩散的趋势。（2）旅游产业集聚能显著提高旅游业碳排放效率，并且具有正向空间溢出效应，而旅游业碳排放效率的负向空间溢出效应则会抑制其他地区旅游业碳排放效率的提高。（3）经济发展、产业结构、城镇化、对外开放、技术进步和环境规制均能不同程度促进旅游业碳排放效率，但城镇化作用效果不显著，旅游业产权结构则显著抑制旅游业碳排放效率，经济发展和城镇化均具有正向空间溢出效应，产业结构呈现出较强的负向空间溢出作用，技术进步和对外开放的正向空间溢出效应并不显著。 Abstract:It is of important practical value to improve the carbon emission efficiency of tourism industry for realizing high-quality development of the tourism industry in the background of carbon neutrality and carbon peaks. Based on the panel data of provinces, autonomous regions, and municipalities in China from 2001 to 2018, the location quotient and a super undesirable slack-based model are applied to measure the tourism agglomeration and tourism carbon emission efficiency, and then to analyze the spatial variation and spatial correlation of the two. Finally, the spatial panel Durbin model is established to study the spatial spillovers of tourism agglomeration on tourism carbon emission efficiency. The results show that:(1) the level of tourism agglomeration in the central and western regions has been improved significantly, while there is no obvious change in the eastern areas; The level of tourism carbon emission efficiency has not changed significantly in other regions except Hebei Province, Shanxi Province, and the Inner Mongolia Autonomous Region. From the trend of the variation, the spatial variation of the two high-level areas mostly spread to the surrounding regions with the existing agglomeration areas as the center. (2) Tourism agglomeration has a significantly positive influence on improving the tourism carbon emission efficiency, and there is also a positively spatial spillover effect. Besides that, the tourism carbon emission efficiency has a negatively spatial spillover effect, which indicates the increase of its own province will have a negative impact on the surrounding provinces. It is possible that the competition of low-carbon technology, talents, information and other resource elements between the surrounding provinces result in the negatively spatial spillover effect. (3) All of the economic development, industrial structure, urbanization, openness, technological progress and environmental regulation can promote the tourism carbon emission efficiency to various degrees, but the effect of urbanization is not significant, and the property right structure of tourism industry has a significantly negative influence on the carbon tourism emission efficiency. Furthermore, the economic development and urbanization has a positively spatial spillover effect, while the industrial structure shows a strongly negative spatial spillover effect. The positive spillover effect of technological progress and openness are not significant. In the future, the positive spillover effect of tourism agglomeration on the tourism carbon emission efficiency should be strengthened. At the same time, we should further improve the positively spatial spillover effect of the economic development and urbanization, and promote the positively spatial spillover effect of openness and technological progress. In addition, accelerating the transform that the property right structure of the tourism industry has a negative impact on the tourism carbon emission efficiency. 参考文献 相似文献 引证文献

Dynamic assessment of agro-industrial sector efficiency and productivity changes among G20 nations

In this study, the Group of 20 (G20; excluding EU economies) were selected as the research objects, and the dynamic network slacks-based model (SBM) was used to evaluate the impact of carbon dioxide (CO2) emissions and forested area on the efficiency and productivity of the industrial and agricultural sectors from 2011 to 2015. Empirical results showed that: (1) The efficiency of the industrial sector was superior to that of the agricultural sector among the G20 countries. Argentina, Australia, Indonesia, Saudi Arabia, South Africa, Turkey, the UK, and the US maintained the best industrial sector efficiency values, falling on the efficiency boundary, whereas Argentina, Brazil, Canada, France, Indonesia, South Korea, Russia, and the US had the best agricultural sector efficiency values. (2) Argentina, Indonesia, and the US had the best overall efficiency value of G20 countries. Saudi Arabia (0.0303), China (0.2721), and the UK (0.2809) had the lowest efficiency values. (3) Only France and Germany had higher than average total factor productivity, while Indonesia and Saudi Arabia had declining industrial and agricultural sector productivity. (4) The proportion of forested area (546.02%) was the most important variable to be improved due to the influence of desert topography, followed by the proportion of agricultural output values (60.86%) and the proportion of industrial output values (38.02%) in some countries.

Trade Openness and Green Total Factor Productivity in China: The Role of ICT-Based Digital Trade

The vigorous development of modern information and communication technology (ICT) has driven the digital trade featured by the ICT technique and industry as the carrier. This study empirically tests the impact of ICT-based digital trade openness on green total factor productivity (GTFP) by selecting ICT as the representative digital trade data of 30 provinces in China over the timespan 2002–2018. We employ the slack-based model and global Malmquist–Luenberger (SBM-GML) estimation method to calculate the provincial GTFP and explore the heterogeneous impact of digital trade openness on GTFP through the scale effect, technology effect, and structure effect. In terms of empirical results, the panel fixed model and panel quantile estimation model both suggest the same findings. With the continuous expansion of the scale of digital trade, its scale effect has a significant inhibitory effect on GTFP, whereas the structure effect combined with human capital and the technology effect correlated with technological research and development (R&D) have a significant promoting effect on GTFP. The panel quantile regression model reveals that the interaction intensity increases gradually from a low quantile to high quantile. Further robustness tests also verify the consistency and stability of the results. Finally, the study puts forward corresponding practical suggestions for the construction of a high-quality open pattern of digital trade and the coordinated development of GTFP. The specific policy implications include the following: (1) Emphasize on the penetration and connection effect of the new generation of ICT, and strengthen the construction of enterprise informatization. (2) Expand digital trade openness and broaden the field of industrial cooperation. (3) Optimize the industrial structure of digital trade, and accelerate the development of core industries of digital trade. (4) Gradually promote the transformation of digital trade from relying on quantity and scale to product quality.