Eco-efficiency Of Industry Research Articles

GLOBAL–LOCAL DRIVING FORCES of CHINA’S GREENING of INDUSTRY

ABSTRACT The greening of industry is an inevitable path for developing economies to achieve sustainability transition. However, the specific roles of global and local driving forces are still not substantially understood. This study develops a three-dimensional framework, which includes coercive pressures, normative pressures, and technology acquisition to analyze the influence of global and local drivers on China’s greening of industry. We measure the industrial eco-efficiency (IEE) using Super-SBM model and analyze its influencing factors using the Tobit regression model at the provincial level in China from 2010 to 2020. China’s IEE evolution and its drivers are stage-specific and regionally differentiated, and differences also exist in the roles of global and local drivers. Developing countries initially start the green development of industry under global normative pressures through integration in the global production network. Thereafter, global coercive pressures and local forces, including domestic technologies, market demands, and effective regulations, drive the further greening of industry. The different potentials of regional coercive and normative pressures depend on industrial structures and socio-technical regimes. Policy recommendations for the greening of industry in developing economies are proposed.

Domesticating sustainability transitions: Spatial and temporal variation of industrial eco-efficiency in China

This study analyses regional industrial sustainability transitions from global and local perspectives. We measure industrial eco-efficiency (IEE) of 280 cities in China from 2010 to 2020 and analyze potential influencing factors using geographical and temporal weighted regression model. We find that foreign investment and international trade are positively associated with IEE with diminishing effects, and then local household consumption enhances IEE on the basis of local environmental governance. The effects of international environmental regulations and technology spillover on IEE are positive, as they align with China's drive for innovation. We further categorize cities into five distinct types based on the influences of both global and local factors—dominated by local environmental regulations, driven by international standards, propelled by foreign investment, shaped by local market dynamics, and affected by multiple factors. We conclude that regional industrial sustainability transitions in China are influenced by forces operating at various scales and exhibit distinct spatiotemporal variation. The drivers of these transitions have shifted from a global scale to a local scale, and both regional conditions and the stage of industrialization significantly affect the intensity of actions undertaken by different scale factors. Finally, we propose policy recommendations, considering regional heterogeneity and the stages of industrialization.

The industrial technological spillover effect and differentiation characteristics of industrial chains in the ecological efficiency of the yellow river basin

The Yellow River Basin (YRB) stands as a critical ecological barrier and a significant economic hub, bridging the western and eastern regions of China. To investigate the synergy of ecoefficiency within the industrial chain of the YRB, this paper introduces a novel research approach. By establishing an industrial technology similarity matrix, spatial econometric models can be employed to analyze spillover effects, which provides a new tool for related research and overcomes some drawbacks of previous studies. The study reveals that industrial ecoefficiency exhibits distinct interaction characteristics across different spatial dimensions. Specifically, the interaction effect within the province is notably superior to that observed within the YRB, as well as in the broader region encompassing the adjacent provinces of the YRB. The upstream and downstream provinces demonstrate a robust level of interaction within their local scopes. However, the middle reaches of provinces exhibit a weak link that undermines the spillover capacity of industrial ecoefficiency across the entire YRB. This finding suggests that hierarchical policy measures should be devised according to the characteristics of spillover effects across various spatial dimensions.

Investigating the effects of industrial transformation and agglomeration on industrial eco-efficiency for green development: Evidence from enterprises in the Yangtze River Economic Belt

Improving industrial eco-efficiency (IEE) is significant for green development, particularly in the areas characterized by intense industrial agglomerations, such as the Yangtze River Economic Belt (YREB). This study analyzes IEE in the middle and lower reaches of the YREB (YREBML) from 2011 to 2020 using the Super SBM-ML model. Based on industrial and commercial enterprise database, this study clarifies the effects of industrial transformation and agglomeration on IEE based on spatial Durbin model. From 2011 to 2020, the IEE in the YREBML exhibited an increased trend, with an average annual increasing rate of 1.48%. The high IEE areas predominantly clustered in eastern coastal cities, while lower IEE regions were mostly located in the central and western areas. Malmquist-Luenberger index decomposition analysis suggests that advanced production technology, improved efficient resource allocation, and optimized industrial structure enhance IEE. The spatial econometric analysis revealed industrial structure rationalization (ISR) and textile industry factory scatter index (FSI) had significant positive effect on IEE, while FSI of chemical raw materials and product manufacturing industry had significant negative effect on IEE. These findings highlight the importance of promoting textile industrial clusters and controlling the quantity and distribution density of chemical industries to improve overall IEE in the YREBML.

An Analysis of the Eco-Efficiency of the Agricultural Industry in the Brazilian Amazon Biome

An Analysis of the Eco-Efficiency of the Agricultural Industry in the Brazilian Amazon Biome

Bootstrap Method of Eco-Efficiency in the Brazilian Agricultural Industry

Bootstrap Method of Eco-Efficiency in the Brazilian Agricultural Industry

Reassessment of industrial eco-efficiency in China under the sustainable development goals: A meta two-stage parallel entropy dynamic DDF-DEA model

As the path of industrial eco-efficiency (IEE) needs to be re-examined from a multi-dimensional perspective, this research opens the internal “black box” of China's industrial eco-economic system driven by sustainable development goals (SDGs) and decomposes IEE into industrial production and sustainable development stages. It further divides the industrial production stage into two parallel subsystems of water use efficiency (WUE) and energy efficiency (EE) and derives SDG6 efficiency (SDG6E) and SDG7 efficiency (SDG7E) in the sustainable development stage respectively. The paper proposes a meta two-stage parallel entropy dynamic DDF-DEA model to re-measure IEE of 30 provinces in China from 2015 to 2021, discusses the relationship between overall efficiency and stage efficiency, and by using the Tobit model explores various types of influencing factors by subregion. The results show that the modified DDF-DEA model can explore China's IEE more accurately under SDGs. China's IEE is more stable from 2015 to 2021, with the efficiency value always fluctuating between 0.59 and 0.64, the overall level is low, and there is more room for improvement. There are significant inter-provincial differences in IEE and efficiency inertia. In more than 75% of the provinces SDG7E constrains IEE improvement, while the dynamic evolution of IEE and its subsystem efficiency varies across regions. Environmental regulation is a common factor affecting IEE across the country and in the east, central, and west regions, whereas the level of economic development, openness to the outside world, and urbanization rate are differential factors affecting IEE across the country and in the three major regions. This study offers new perspectives for exploring the path of IEE enhancement and providing empirical references for the collaborative construction of industrial ecological civilization.

Empirical analysis of factors influencing industrial eco-efficiency in the Yellow River Basin from a social embeddedness perspective

Measuring industrial eco-efficiency (IEE) is essential to improve environmental quality and industrial restructuring. However, most studies ignore the influence of embeddedness on industrial eco-efficiency and lack analysis of the pathways of influence factors. Therefore, this study assesses industrial eco-efficiency in the Yellow River Basin (YRB) using a super-efficiency model slacks-based measure (Super-SBM) that considers non-desired outputs, outlines the social embeddedness of IEE, and empirically analyzes the driving mechanism of IEE from the perspective of embeddedness by constructing hierarchical linear modeling (HLM) to address the pathways of action of the influencing factors of industrial eco-efficiency. The results showed that 50.79% of the overall differences in IEE in the YRB were caused by social embeddedness. Economic development level, industrial agglomeration, and environmental regulation (ER) are significant direct influencing factors. Increasing cognitive and cultural embeddedness will enhance the positive relationship between economic development level and IEE. Political and relational embeddedness significantly moderates the positive relationship between industrial agglomeration and eco-efficiency. Cultural embeddedness can significantly and directly affect industrial eco-efficiency and weaken the positive relationship between ERs and industrial eco-efficiency. Therefore, improving IEE should consider both fundamental and embedded factors. Our findings are conducive to promoting high-quality development in the YRB and support the government in formulating differentiated policies. In addition, this paper tries to establish an empirical analysis method suitable for social embeddedness theory, and the empirical results help to improve the situation due to the lack of empirical analysis of social embeddedness theory.

How industry integration improves the eco-efficiency of the textile industry: The case of Qun-yi village in China

During the formulation of policies related to China's Rural revitalization strategy in recent decades, industries (e.g., the textile industry) with high environmental restoration costs and low added value of social and economic benefits have been transformed or phased out to achieve eco-efficiency. In the policy implementation, a new self-organized social network of village committees, entrepreneurs, enterprises and villagers has been established to support village-level self-organization. Aiming to explore the nexus of industry integration and eco-efficiency for industry development in rural areas, we conducted a longitudinal single case study Qun-yi Village. In the evolution process from 1995 to 2022, the textile industry integration process of the Village was divided into three periods. We found that first comprehensive industrial integration characterized by increasing complex industry structures and blurred industry boundaries, has greatly improved eco-efficiency by reducing economic and environmental restoration costs and increasing output value. Second, a successful village-level self-organization featured by strong village committee leadership, participation of actors and perceived benefits significantly facilitate and moderate the process of industrial integration aiming for high eco-efficiency. This study provided important theoretical and policy insights into the industrial integration for high eco-efficiency in rural areas in developing countries from the perspective of village-level self-organization.

How does digital infrastructure affect industrial eco-efficiency? Considering the threshold effect of regional collaborative innovation

The implementation of Digital China and Made in China 2025 strategies has brought guiding the rapid development of digital infrastructure (DI) and industrial ecology into focus. Based on Beijing–Tianjin–Hebei (BTH) city data from 2011 to 2020, this study empirically investigates the influence mechanism and spatial effect of DI and regional collaborative innovation (RCI) on industrial eco-efficiency (IEE) using the improved gravity model and super-efficiency slacks-based measure model. The relevant findings are as follows: (1) Static and dynamic analyses show that DI significantly improves IEE. (2) The moderating effect test shows that RCI strengthens the effect of DI on IEE. (3) The threshold effect test captures a U-shaped nonlinear relationship between DI and IEE at different RCI intervals. In the positive stage, the improvement effect of DI on IEE presents marginal increasing characteristics. (4) For the spatial perspective test, the BTH city's DI and IEE exhibit a positive spatial correlation, and gradually form H–H and L–L cluster states. Furthermore, the city's DI improves the local city's IEE and benefits neighboring city's IEE, revealing spatial spillover effect. This study has valuable reference significance for other regions to simultaneously solve industrial development and environmental problems by advancing DI.

The Impact of the Digital Economy on Industrial Eco-Efficiency in the Yangtze River Delta (YRD) Urban Agglomeration

Enhancing industrial eco-efficiency (IEE) represents an inherent imperative in the pursuit of green, low-carbon, and high-quality development. The burgeoning digital economy (DE) contributes to the digitization and intelligent integration and transformation of production and consumption, which contributes to enhancing economic efficiency and resource utilization efficiency while reducing carbon emissions and the environmental pollution associated with industrial production and providing important support for the ecological transformation of industries. In this context, using data from the YRD urban agglomeration and employing the Tobit model and the spatio-temporal correlation model, this study analyzes the impact of the digital economy on industrial eco-efficiency. The findings are as follows: (1) The correlation over time between IEE and the DE followed an inverted V-shaped trend, while the relationship between the DE and pure technical efficiency (PTE) exhibited a fluctuating W-shaped pattern. The spatial correlation revealed Zhejiang province as the primary concentration of positive correlation between the DE and both IEE and PTE. (2) The development of the DE had a significant positive impact on IEE, not only directly but also indirectly through promoting green technological innovation and advancing industrial structure. (3) The analysis of regional heterogeneity showed that the development of the DE in core cities played a catalytic role in improving IEE, whereas the impact of the DE on IEE was not significant in outer cities. This research not only offers new views on how to develop industry in more environmentally friendly ways, but it also sheds light on the real effects of the digital economy on high-quality urban development.

Industrial eco-efficiency of resource-based cities in China: spatial-temporal dynamics and associated factors.

Promoting the greening of industry is the key to achieving high-quality and sustainable development of the urban economy. It is particularly important for resource-based cities (RBCs) that exploit natural resources as the leading industries. In this paper, the Windows-Bootstrap-DEA model was used to calculate the industrial eco-efficiency (IEE) of 114 RBCs in China from 2003 to 2016, and the regional differences and dynamic evolution characteristics of the IEE were analyzed. The panel Tobit model was used to explore the factors associated with IEE in RBCs. The results showed that the IEE of RBCs in China was at a low level during the study period, and the resource utilization process had not reached an optimal state. There were large regional differences in IEE, and there was a significant degree of spatial agglomeration. The results of conditional probability density estimation showed that the distribution of IEE had strong internal stability on the whole, and the distributions of IEE of RBCs in different regions, different resource types, and different development stages showed significant differences. The results of the panel Tobit model showed that per capita GDP, ownership structure, science and technology input, and industrial agglomeration had significant positive effects on IEE, while industrial structure and employment structure showed significant negative effects. The conclusions of this paper can provide a scientific decision-making basis for industrial transformation planning of RBCs.

Spatial Disequilibrium and Dynamic Evolution of Eco-Efficiency in China’s Tea Industry

Eco-efficiency is a significant target for evaluating the agricultural ecosystem and measuring sustainable agricultural development through quantitative analysis. It is also an essential part of constructing the ecological tea garden, which offers a directional function in realizing the green development of the tea industry. After measuring the eco-efficiency of China’s tea industry using the super-efficiency SBM model, this paper analyzes the spatial disequilibrium and dynamic evolution trend of the eco-efficiency in China’s tea industry through the method of Dagum Gini Coefficient and Kernel Density Estimation. The results show that the level of eco-efficiency in China’s tea industry was improved overall, and the spatial disequilibrium was significantly reduced. The differences within the tea region decreased as follows: tea regions in Southwest China, South China, south of the Yangtze River, and north of the Yangtze River; the overall difference in the eco-efficiency in the tea industry mainly comes from the contribution of the interregional difference in tea regions, and the second contribution comes from the intraregional difference in tea regions and the difference in super-variable density. The eco-efficiency of the tea industry has been improved both nationally and within the top four tea regions; the disequilibrium between areas and within the tea region has been largely alleviated, but there is still room to optimize the input–output structure and promote the eco-efficiency.

Effect of activated coal waste and treated Pinus fibers on the physico-mechanical properties and durability of fibercement composites

The constant environmental and social pressure on the search for alternative and clean technologies has strongly influenced the construction industry, which is considered as one of the main contributors to global warming due to the emission of carbon dioxide (CO2). The objective of this study was to evaluate the effect of partial replacement of cement by activated coal waste on cement-based composites reinforced by unbleached pine pulps. The results showed that the hornification treatment reduces the water absorption capacity and increases the dimensional stability of the cellulosic pulp. In addition, the thermal treatment preserves the original chemical composition of the vegetable pulp. Moreover, the partial replacement of cement by activated coal waste (ACW) increased the mechanical performance. The results of thermogravimetric analysis showed that composites with ACW present a decrease of the calcium hydroxide content compared to composites containing limestone. After accelerated aging tests, a decrease in the reinforcement capacity of the fiber and densification of the material is observed. However, composites with treated pulps and activated coal waste showed higher mechanical properties and a greater durability. According to the results, the combination of the hornification treatment and the partial replacement of cement by ACW can be used to improve the eco-efficiency of the fiber cement industry. The combination of both approaches allows to a reduction the carbon dioxide emissions (reduction of cement consumption) and an increase of the durability of the material.

Does industrial upgrading improve eco-efficiency? Evidence from China's industrial sector

Promoting industrial upgrading is the fundamental path for China to realize the coordination of industrial development and ecological protection. This paper firstly measures the industrial upgrading index (IUI) and eco-efficiency of 37 sub-industries in China's industrial sector based on the comprehensive weighting method and super-efficiency slacks-based measure model, and then for the first time, examines the impact of industrial upgrading on China's industrial eco-efficiency and its differences among industries and influential mechanisms using the panel data models. The following main conclusions are obtained. First, both the IUI and eco-efficiency of China's whole industrial sector showed an upward trend from 2005 to 2019. Second, there are significant gaps in the IUI and eco-efficiency among industries, and these gaps gradually increase in 2005–2019. Third, industrial upgrading significantly improves China's industrial eco-efficiency, and the results of robustness test also support this conclusion. Fourth, industrial upgrading positively affects the eco-efficiency of both high-pollution and low-pollution industries, and the positive effect on low-pollution industries is slightly greater than that on high-pollution industries. Fifth, industrial upgrading improves industrial eco-efficiency by enhancing production efficiency and energy utilization efficiency and optimizing energy consumption structure.

How has China’s industrial eco-efficiency been improved? Evidence from multi-scale countrywide study

The process of industrialization often causes resource depletion and environmental pollution. To shed light on China's resource use and pollution trends in the context of the country's rapid industrial growth, this study analyzes the eco-efficiency of China's industry from 2000 to 2015. We quantify industrial eco-efficiency (IEE) for China and its provinces using data envelopment analysis (DEA) and analyze potential influencing factors at national and regional levels using Tobit regression. IEE in China and in most provinces shows a clear upward trend with some fluctuations, with national scores increasing from 0.394 to 0.704. There is strong regional disparity, with average IEE scores in eastern provinces (0.840) higher than those in central provinces (0.625), which are in turn higher than those in the northeast (0.537) and west (0.438). We next consider potential drivers. Economic development and foreign direct investment (FDI) are positively associated with IEE but appear to show diminishing returns. Environmental enforcement and market for technology are also positively associated with IEE, as expected. The impact of economic development, industrial sector structure, and investment in research and development (R&D) are modified by the stage of industrialization in each region. Targeted measures that can adjust industry structure, enhance environmental enforcement, attract FDI, and increase R&D investment may help further improve IEE in China.

The spatio‐temporal interactive effects between ecological urbanization and industrial ecologization in the Yangtze River Delta region

AbstractIn this study, we investigated the spatio‐temporal synergistic relationship between ecological urbanization (EU) and industrial eco‐efficiency (IEE) in the context of the integrated development of the Yangtze River Delta region. The main findings are as follows: (1) EU and IEE generally exhibited similar spatial distribution patterns, with high values concentrated in the southern part of the delta and low values in the northern part and Anhui Province; there was also a high degree of coupling between the two systems. (2) IEE and EU's relationship presented a U‐shaped positive correlation trend from 2001 to 2019, and a positive correlation was observed in most cities, with highly correlated cities clustered in southern Zhejiang and Anhui province, while negative correlation cities scattered in a few cities in northern Zhejiang, northern Anhui, and southern Jiangsu. (3) Significantly spatial association characteristics were found between EU and IEE, with HEUHIEE type clustering mainly in southeastern delta cities, while the LEULIEE type slowly encroached upon northwest delta cities. (4). Synergistic promoting effects were identified between IEE and EU, whereby, the improvement of IEE in both local and neighboring cities contributed to promoting EU, and the progress of EU in both local and neighboring cities helped to promote IEE.

Study on the measurement of industrial eco-efficiency, spatial distribution and influencing factors in Yangtze River Economic Belt.

Nowadays, China insists on the strategy of ecological priority and green development, and the Yangtze River Economic Belt is a pioneering demonstration belt for the construction of ecological civilization in China. Promoting industrial ecological efficiency is of great significance to Chinese sustainable development and high-quality economic development. Based on the provincial panel data of 11 provinces and cities in the Yangtze River Economic Belt region from 2011 to 2020, we use the super-efficient slacks-based measure of efficiency SBM model to measure the industrial eco-efficiency of the Yangtze River Economic Belt, reveal the spatial differences in industrial eco-efficiency of different provincial units, and explore the industrial eco-efficiency. The results show that: (1) from the perspective of the Yangtze River Economic Belt as a whole, industrial eco-efficiency is on a stable upward trend, but the efficiency level is not high, and there is more room for improvement; (2) in terms of the spatial distribution of industrial eco-efficiency in the Yangtze River Economic Belt, there are significant differences, with industrial eco-efficiency in the downstream region being higher than that in the midstream region and the upstream region, and the lowest in the midstream region, while in terms of the spatial distribution pattern, the industrial eco-efficiency of 11 provinces and cities in the Yangtze River Economic Belt eco-efficiency has a significant positive spatial autocorrelation; (3) in terms of influencing factors, economic development level, factor endowment, and foreign investment can significantly influence industrial eco-efficiency in the Yangtze River Economic Belt. The study's result helps provide theoretical guidance and practical reference for promoting the green and ecological development of industries in the Yangtze River Economic Belt.

Data-Driven Eco-Efficiency Analysis and Improvement in the Logistics Industry in Anhui.

The ecological efficiency (eco-efficiency) of a regional logistics industry (RLI) is widely regarded as a key factor affecting sustainability of economic development, environmental protection, and resources utilization. This study applied a data-driven method to evaluate and increase the eco-efficiency of an RLI. Based on RLI-related data, which were converted into proper dimensionless indices, data envelopment analysis (DEA), which assumes that the decision-making units (DMUs) are in the situation of variable returns to scale, the Banker, Charnes, and Cooper (BCC) model, and Malmquist index model were used to assess the eco-efficiency of the RLI from both static and dynamic viewpoints. Then, a Tobit regression model was built to explore the factors that influence eco-efficiency. The effectiveness of this approach was verified by its application to an example from Anhui Province. This study has theoretical and practical value for the assessment and promotion of the ecological eco-efficiency of the RLI. We believe that our approach offers a powerful tool to assist logistics enterprises and local governments in coordinating the relationship between the RLI economy and the ecological environment, facilitating the drive to carbon neutrality.

Examining energy eco-efficiency in China's logistics industry

Carbon dioxide (CO2) emission significantly affects sustainability efforts, calling for improvements in energy eco-efficiency (EEE). In this study, we use three methods—input–output indicators, projection pursuit regressions, and neural networks—to investigate the CO2 emission characteristics of the logistics industry in China and its provinces from 2010 to 2019. We find influential factors of carbon emissions and their corresponding efficiencies in various provinces of China. Our results reveal excess inputs and deficient outputs and the relationship between economic development levels and the environmental quality. Based on the results, we provide technical measures and managerial implications for effectively reducing CO2 emissions and improving EEE.