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Keywords = SBM–Malmquist model

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16 pages, 3897 KiB  
Article
Green Economic Efficiency and Coordinated Development in the Bohai Rim Region: Addressing Regional Disparities for Sustainable Innovation and Economic Transformation
by Kun Xiao, Xiaolong Chen, Hongfeng Zhang and Cora Un In Wong
Sustainability 2025, 17(3), 932; https://doi.org/10.3390/su17030932 - 23 Jan 2025
Viewed by 622
Abstract
The Bohai Rim Region plays a crucial role in the economy of northern China. Historically, the area’s development has been driven by resource-intensive industries, necessitating urgent structural transformation. In response, the government has actively promoted a green economic model. This study evaluates the [...] Read more.
The Bohai Rim Region plays a crucial role in the economy of northern China. Historically, the area’s development has been driven by resource-intensive industries, necessitating urgent structural transformation. In response, the government has actively promoted a green economic model. This study evaluates the efficiency of green economic performance and total factor productivity (TFP) across five provinces in the region, incorporating regional innovation capabilities and green innovation outputs into the green economy input–output system. The results show that green economic efficiency (GEE) has improved across regions, with higher technological advancement leading to greater improvements in green TFP. Additionally, the economic disparity between provinces and municipalities has been decreasing. This study indicates that, while inter-provincial differences are widening, intra-regional disparities are narrowing. Meanwhile, this study provides a foundation for regional economic integration and policymaking in the Bohai Rim, offering insights into balancing economic growth with environmental sustainability. Full article
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<p>The geographical composition of the bohai rim region.</p>
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<p>Model framework for the green economy.</p>
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<p>(<b>a</b>) Change trends of GEE curves for the municipalities of Beijing and Tianjin; (<b>b</b>) change trends of GEE curves for the provinces of Hebei, Liaoning, and Shandong.</p>
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<p>(<b>a</b>) Contribution of GEC and GTC to GML changes from 2011 to 2012 in Beijing; (<b>b</b>) contribution of GEC and GTC to GML changes from 2011 to 2012 in Tianjin; (<b>c</b>) contribution of GEC and GTC to GML changes from 2011 to 2012 in Hebei; (<b>d</b>) contribution of GEC and GTC to GML changes from 2011 to 2012 in Liaoning; (<b>e</b>) contribution of GEC and GTC to GML changes from 2011 to 2012 in Shandong.</p>
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<p>Trends in Theil index and regional disparities (2011–2022).</p>
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25 pages, 4059 KiB  
Article
Analysis of the Dynamic Changes and Driving Mechanism of Land Green Utilization Efficiency in the Context of Beijing–Tianjin–Hebei Synergistic Development
by Huizhen Cui, Linlin Cheng, Yang Zheng, Junqi Wang, Mengyao Zhu and Pengxiang Zhang
Land 2025, 14(2), 222; https://doi.org/10.3390/land14020222 - 22 Jan 2025
Viewed by 420
Abstract
Studying the development of land green utilization efficiency and the factors that influence it in the Beijing–Tianjin–Hebei region can improve the distribution of land resources among regions and reinforce interregional integrated planning. By constructing a super-efficiency SBM model, calculating the Malmquist–Luenberger index, and [...] Read more.
Studying the development of land green utilization efficiency and the factors that influence it in the Beijing–Tianjin–Hebei region can improve the distribution of land resources among regions and reinforce interregional integrated planning. By constructing a super-efficiency SBM model, calculating the Malmquist–Luenberger index, and constructing a Tobit model, this study explores the spatial features and temporal variations of land green use efficiency in the Beijing–Tianjin–Hebei region from 2010 to 2022. It also examines the mechanism that drives land green use efficiency in the context of the Beijing–Tianjin–Hebei synergistic development. According to this research, Beijing has consistently had the highest land green usage efficiency and a strong green development strength, whereas Baoding, Xingtai, Handan, and other cities in Hebei Province have lower land green utilization efficiency. According to the geographical dimension, the research area’s land green use efficiency exhibits a pattern of “high in the middle and low in the surroundings”, with Cangzhou, Langfang, and Tangshan standing out in terms of both industrial transformation and ecological building. Based on the results of the driving mechanism of land green use efficiency, it is evident that while the degree of urbanization and population concentration has a negative effect on land green use efficiency, the degree of economic development, industrial synergy, opening up to the outside world, environmental regulation, and ecological output all have positive and promoting associations with it. In summary, increasing the optimization of the economic and industrial structure, bolstering technological innovation and policy coordination, and attaining a harmonious coexistence of the economy and ecology are all essential steps in the process to increase the land green use efficiency in the research area when attempting to achieve the goal of sustainable development in the region. Full article
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<p>Overview of the research area.</p>
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<p>Land green utilization efficiency values in the Beijing–Tianjin–Hebei region from 2010 to 2022.</p>
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<p>Spatial distribution of land green use efficiency in the Beijing–Tianjin–Hebei region.</p>
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<p>Spatial distribution of total factor productivity.</p>
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<p>Spatial distribution of technical efficiency.</p>
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<p>Spatial distribution of technological progress.</p>
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<p>Spatial distribution of pure technical efficiency.</p>
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<p>Spatial distribution of scale efficiency.</p>
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17 pages, 1823 KiB  
Article
Can Environmental Protection Tax Promote Urban Green Transformation? Experimental Evidence from China
by Zhankun Qi, Feng Long, Fenfen Bi, Xue Tian, Ziwei Qian, Xianming Duan and Chazhong Ge
Sustainability 2024, 16(20), 9011; https://doi.org/10.3390/su16209011 - 17 Oct 2024
Viewed by 1361
Abstract
As one of China’s important environmental and economic policies, the environmental protection tax (EPT) is important in promoting economic and social green transformation. In this study, the green total factor productivity (GTFP) of 283 prefecture-level cities in China from 2013 to 2022 was [...] Read more.
As one of China’s important environmental and economic policies, the environmental protection tax (EPT) is important in promoting economic and social green transformation. In this study, the green total factor productivity (GTFP) of 283 prefecture-level cities in China from 2013 to 2022 was calculated using a Super Slack-Based Model (Super-SBM) and the Malmquist-Luenberger (ML) index, which includes undesirable outputs. Moreover, the implementation effect of environmental tax on promoting urban green transformation is identified through the difference-in-differences (DID) model. This study revealed that (1) an EPT can significantly increase the GTFP of a city and promote its green transformation. (2) Industrial structure optimization and technological innovation are important mechanisms through which EPT drives urban green transformation. (3) The implementation effect of EPT in promoting urban green transformation presents significant policy differences across geographic locations, whether cities are key environmental protection cities or types of resource-based cities. EPT can significantly promote the green transformation of local cities, which in turn can positively affect the green transformation of neighboring cities. Based on this study’s conclusions, suggestions are put forward to improve the EPT system to promote urban green transformation. Full article
(This article belongs to the Special Issue Environmental Governance and Environmental Responsibility Research)
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<p>Effect path of the EPT on urban green transformation.</p>
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<p>Parallel trend test.</p>
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<p>Placebo test.</p>
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<p>PSM effect.</p>
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19 pages, 5903 KiB  
Article
Spatial Interaction Spillover Effect of Tourism Eco-Efficiency and Economic Development
by Qi Wang, Qunli Tang and Yingting Guo
Sustainability 2024, 16(18), 8012; https://doi.org/10.3390/su16188012 - 13 Sep 2024
Viewed by 1272
Abstract
Tourism eco-efficiency (TEE) is a pivotal metric for assessing tourism’s sustainability and the balance between human activities and the environment, significantly influencing regional economic growth (RGDP). This research utilizes a comprehensive analytical framework, combining the Super SBM-DEA model, the Malmquist index, and spatial [...] Read more.
Tourism eco-efficiency (TEE) is a pivotal metric for assessing tourism’s sustainability and the balance between human activities and the environment, significantly influencing regional economic growth (RGDP). This research utilizes a comprehensive analytical framework, combining the Super SBM-DEA model, the Malmquist index, and spatial econometric models, to analyze the spatial interplay between TEE and RGDP within the Yangtze River Economic Belt (YREB) from 2009 to 2021. The results show that (1) TEE in the YREB exhibits a generally upward trajectory with fluctuations, with upstream and downstream regions consistently outperforming the midstream areas in terms of efficiency; (2) technological progress is identified as the primary driver behind efficiency variations; (3) and there exists a symbiotic relationship between local TEE and RGDP, where the economic prosperity of adjacent regions exerts a competitive pull on local TEE, while the TEE of neighboring areas can slow down local economic growth. The study concludes with strategic recommendations aimed at fostering regional collaborative advancement, offering valuable insights for the sustainable development agenda of nations and regions. Full article
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<p>Theoretical framework diagram of the interaction mechanism between TEE and RGDP.</p>
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<p>The study area. Produced by the author of this paper. The base map is sourced from the standard map service system (<a href="http://211.159.153.75/index.html" target="_blank">http://211.159.153.75/index.html</a>, accessed on 6 July 2024), with a review number of GS (2020) 4619, and no modifications have been made to the map elements.</p>
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<p>Temporal and regional evolution of TEE in the YREB, 2009–2021. (<b>a</b>) shows the overall TEE and its changing trends in the three major regions of the YREB from 2009 to 2021; (<b>b</b>) displays the TEE and its changing trends at the provincial and municipal levels in the 11 provinces and cities of the YREB from 2009 to 2021.</p>
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<p>Spatial distribution of TEE in the YREB, 2009–2021. The darker the color, the higher the efficiency value during the same period.</p>
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<p>(<b>a</b>,<b>b</b>) Line chart of Malmquist model decomposition for TEE by year and region in the YREB.</p>
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19 pages, 539 KiB  
Article
A Comparative Analysis of Performance Efficiency for the Container Terminals in China and Korea
by Jin Zhang, Shuyin Deng, Yulseong Kim and Xuebin Zheng
J. Mar. Sci. Eng. 2024, 12(9), 1568; https://doi.org/10.3390/jmse12091568 - 6 Sep 2024
Cited by 2 | Viewed by 1137
Abstract
In this study, the static and dynamic performance efficiencies of container terminals are analyzed and compared for the main container terminals in China and Korea. The static performance efficiency is calculated using the Super-SBM model based on slack variables at the micro-level. An [...] Read more.
In this study, the static and dynamic performance efficiencies of container terminals are analyzed and compared for the main container terminals in China and Korea. The static performance efficiency is calculated using the Super-SBM model based on slack variables at the micro-level. An analysis on the dynamic performance efficiency is conducted with the Malmquist index method. The factors of scale and technology of container terminals are mainly taken into account to explore the performance efficient improvement path of container ports. We obtained the following conclusions: (1) The container terminals in Korea show a similar performance efficiency level to the terminals in China, and their performance efficiency is an overall upward trend over the past five years. (2) The main reason for inefficiency in the container terminals in China and Korea is predominantly scale inefficiency. (3) Boosting the automation degree does not have a completely positive impact on the efficiency of the terminal. (4) In 2019–2023, the technical progress index of all container terminals in China and Korea showed a decreasing trend, leading to performance inefficiency of the container terminals. Full article
(This article belongs to the Special Issue Future Maritime Transport: Trends and Solutions)
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<p>Heat map of partial slack results.</p>
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24 pages, 1377 KiB  
Article
The Impact of Human Capital and Tourism Industry Agglomeration on China’s Tourism Eco-Efficiency: An Analysis Based on the Undesirable Super-SBM-ML Model
by Qiao Wang, Meixian Wei, Nan Wang and Qiuhua Chen
Sustainability 2024, 16(16), 6918; https://doi.org/10.3390/su16166918 - 12 Aug 2024
Viewed by 1457
Abstract
Tourism eco-efficiency has played a significantly essential role in the sustainable development of tourism destinations and tourism industries, providing ideal inputs and outputs amidst the deepening environmental crisis. This study evaluates the development level of tourism eco-efficiency using the Super-SBM model with undesirable [...] Read more.
Tourism eco-efficiency has played a significantly essential role in the sustainable development of tourism destinations and tourism industries, providing ideal inputs and outputs amidst the deepening environmental crisis. This study evaluates the development level of tourism eco-efficiency using the Super-SBM model with undesirable outputs, employing the Malmquist-Luenberger (ML) index to analyse the internal optimisation forces of tourism eco-efficiency. Furthermore, human capital is assessed through both horizontal and vertical education levels, followed by a panel Tobit econometric analysis to explore the external impact mechanisms on tourism eco-efficiency. The results show that (1) Technological advancement is the core intrinsic driver for optimising tourism eco-efficiency. (2) In the analysis of influencing mechanisms, Human capital significantly contributes to enhancing tourism eco-efficiency, a conclusion upheld even after conducting robustness tests. (3) Analysis of mediating mechanisms indicates that tourism industry agglomeration is a critical pathway through which human capital enhances tourism eco-efficiency. This correlation has been proven reliable by regional regression analysis. (4) Results of the threshold model test suggest a law of “increasing marginal effect” concerning the positive impact of human capital on tourism eco-efficiency within the regulation of tourism industry agglomeration. Consequently, regions should actively promote the roles of human capital and tourism industry agglomeration in advancing tourism eco-efficiency, improving resource utilization efficiency, and tourism industry specialization to foster sustainable tourism development. Full article
(This article belongs to the Section Tourism, Culture, and Heritage)
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<p>The theoretical model.</p>
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<p>Tourism eco-efficiency and human capital development levels by region from 2011 to 2020.</p>
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<p>Average ML indices of tourism eco-efficiency and their decomposition for each year in china from 2011 to 2020.</p>
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24 pages, 2241 KiB  
Article
Measurement of Tourism Ecological Efficiency and Analysis of Influencing Factors under the Background of Climate Change: A Case Study of Three Provinces in China’s Cryosphere
by Yubin Wu, Feiyang He, Zhujun Sun and Yongyu Wang
Sustainability 2024, 16(14), 6085; https://doi.org/10.3390/su16146085 - 16 Jul 2024
Viewed by 1257
Abstract
Against the backdrop of climate change and the “dual carbon” goals, enhancing the ecological efficiency of cryospheric tourism is crucial not only for the high-quality development of the tourism industry itself but also for the protection of the ecological environment and the promotion [...] Read more.
Against the backdrop of climate change and the “dual carbon” goals, enhancing the ecological efficiency of cryospheric tourism is crucial not only for the high-quality development of the tourism industry itself but also for the protection of the ecological environment and the promotion of green sustainable development in the cryospheric region. In light of this, this study, taking climate change as its background and based on the perspective of carbon emission constraints, integrates multidimensional factors such as “climate change, carbon emission constraints, and cryospheric resources” into a unified measurement framework to construct a model for evaluating the ecological efficiency of tourism in the cryosphere. Specifically, the model considers inputs, expected outputs, and unexpected outputs. Subsequently, employing the super-efficiency slack-based measure (SBM) model, this study measures the tourism ecological efficiency (TEE) of three provinces (Xinjiang, Qinghai, Tibet) in the cryosphere from 2013 to 2021 and utilizes the Malmquist–Luenberger index and gray correlation model to reveal their dynamic changes, efficiency decomposition, and influencing factors. The results indicate that: (1) The overall mean of TEE in the cryosphere is between 0.2428 and 1.2142, Over the study period, the average annual growth rate and corresponding confidence interval were 14.74%, (−8.61%, 64.23%), showing a significant fluctuating growth trend. Among them, Xinjiang stands out, with its mean scores ranging from 0.2418 to 1.6229, surpassing the overall average level of the cryosphere. (2) During the study period, the overall dynamic efficiency of tourism ecology in the cryosphere increased by 21.54%, driven by the synergy of technological progress (TC), pure technical efficiency (PET), and scale efficiency (SE). For each province, the dynamic efficiency of tourism ecology has improved, but to varying degrees. (3) Regarding the driving factors of TEE in the cryosphere, each driving factor is closely related to TEE, ranked from large to small as follows: carbon emission structure, level of economic development, infrastructure, intensity of technological input, industrial structure, resource endowment, and environmental regulation. This article holds theoretical and practical significance for promoting the high-quality development of polar tourism and achieving synergistic progress between the economy and environment. Full article
(This article belongs to the Special Issue Climate Change Impacts and Sustainable Tourism)
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<p>Number of domestic and foreign tourists received. Data source: statistics for each province for the 2021 National Economic and Social Development (<a href="https://www.xzxw.com/" target="_blank">https://www.xzxw.com/</a>, <a href="https://www.xinjiang.gov.cn/" target="_blank">https://www.xinjiang.gov.cn/</a>, <a href="http://tjj.qinghai.gov.cn/" target="_blank">http://tjj.qinghai.gov.cn/</a>, all accessed on 3 November 2023).</p>
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<p>Distribution of glaciers and ski resorts in the cryosphere. Note: The standard map No. GS (2023) 2767 downloaded from the standard map service website of the National Bureau of Surveying, Mapping and Geographic Information is made, and the base map is not modified.</p>
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<p>Trends in tourism carbon emissions and energy consumption.</p>
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<p>Dynamic changes of ML index in each province of the cryosphere.</p>
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<p>Dynamic change of decomposition efficiency in the cryosphere.</p>
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<p>Differences in the correlation degree of driving factors of tourism eco-efficiency.</p>
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19 pages, 614 KiB  
Article
Can Environmental Regulation Enhance Green Total Factor Productivity?—Evidence from 107 Cities in the Yangtze River Economic Belt
by Mengli Liu, Yan Zhu and Jingjing Zhang
Sustainability 2024, 16(12), 5243; https://doi.org/10.3390/su16125243 - 20 Jun 2024
Cited by 1 | Viewed by 1363
Abstract
Promoting green development has emerged as a pivotal approach to optimizing the ecological and economic structure, thereby fostering sustainable development. Whether the implementation of environmental regulations in the Yangtze River Economic Belt (YREB), an important economic corridor in China, has increased the green [...] Read more.
Promoting green development has emerged as a pivotal approach to optimizing the ecological and economic structure, thereby fostering sustainable development. Whether the implementation of environmental regulations in the Yangtze River Economic Belt (YREB), an important economic corridor in China, has increased the green total factor productivity (GTFP) of cities remains to be investigated. This paper uses Chinese city panel data from 2007 to 2019 to calculate the green total factor productivity (GTFP) of 107 cities in the Yangtze River Economic Belt using the super-efficiency SBM (Slacks-Based Measure) model and the GML (Global Malmquist–Luenberger) index and measures the intensity of environmental regulations through textual analysis. Through empirical analyses, this paper finds that environmental regulation has an inverted U-shaped effect on green total factor productivity (GTFP), which is first promoted and then suppressed, and the inflection point of the inverted U-shaped curve is about 0.51. Mechanism analyses show that environmental regulation in the Yangtze River Economic Belt promotes the growth of GTFP by facilitating green technological innovation but does not improve GTFP by enhancing the level of industrial structure. Heterogeneity analyses show that the effect of environmental regulation on GTFP is more significant in the city clusters in the middle and upper reaches of the Yangtze River and in cities outside the city clusters. Therefore, when formulating environmental regulation policies, the relationship between economic development and environmental protection should be balanced, while focusing on regional heterogeneity and adapting to local conditions, to coordinate the environment and economic development of the whole Yangtze River basin. Full article
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<p>Fitted plot of environmental regulation and green total factor productivity.</p>
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27 pages, 6775 KiB  
Article
Impacts of Cropland Utilization Patterns on the Sustainable Use Efficiency of Cropland Based on the Human–Land Perspective
by Xinyu Hu, Chun Dong and Yu Zhang
Land 2024, 13(6), 863; https://doi.org/10.3390/land13060863 - 15 Jun 2024
Cited by 4 | Viewed by 1188
Abstract
Confronted with China’s burgeoning population and finite arable land resources, the enhancement of sustainable arable land efficiency is of paramount importance. This study, grounded in the United Nations Sustainable Development Goals (SDGs), introduces a robust framework for assessing sustainable arable land use. Utilizing [...] Read more.
Confronted with China’s burgeoning population and finite arable land resources, the enhancement of sustainable arable land efficiency is of paramount importance. This study, grounded in the United Nations Sustainable Development Goals (SDGs), introduces a robust framework for assessing sustainable arable land use. Utilizing the Sustainable Utilization of Arable Land (SUA) indicator system, the DGA–Super-SBM model, the Malmquist–Luenberger production index, and the TO–Fisher–OSM algorithm, we evaluated the efficiency of sustainable utilization of arable land (ESUA) in 52 prefecture-level cities within China’s major grain-producing regions of the Yellow and Huaihai Seas. We analyzed the cropland utilization patterns from 2010 to 2020, examining the influence of these patterns on sustainable utilization efficiency. Our findings indicate that between 2010 and 2020, the arable land usage in these regions exhibited minimal transformation, primarily shifting towards construction land and conversely from grassland and water systems. Notably, the ESUA of arable land demonstrated an upward trend, characterized by pronounced spatial clustering, enduring high efficiency in the northern regions, and a significant surge in the southern sectors. The declining ESUA (D-ESUA) trend was general but increased in half of the cities. The change in the center of gravity of ESUA correlated with the north–south movement of the proportion of cultivated land area, the turn-in rate, and the turn-out rate, yet moved in the opposite direction to that of cultivated land density and yield per unit area. Variables such as the replanting index, cropland density, yield per unit area, and cropland turn-in rate significantly affected ESUA. These findings offer a scientific basis and decision-making support for optimizing the utilization pattern of arable land and achieving a rational allocation of arable land resources. Full article
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<p>Technology roadmap.</p>
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<p>Location map of the project area. Note: This map is based on a standard map [review number GS(2020)4619], retrieved from the Ministry of Natural Resources’ standard map service website. The base map is unmodified.</p>
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<p>Arable land use transfer chord, 2000–2020.</p>
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<p>Spatial pattern of changes in cropland use, 2010–2020.</p>
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<p>Similarity tests. Note: In the figure, the trend and the amount of information of DGA–Super-SBM and Super-SBM are basically kept the same, while the difference of Projective tracing algorithm is larger.</p>
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<p>ESUA distribution in the Yellow–Huai–Hai River Basin.</p>
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<p>Temporal changes in average ESUA in different regions of the YHHRB.</p>
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<p>Temporal changes in dynamic sustainable efficiency of cropland for different provinces of the YHHRB.</p>
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<p>Temporal changes in mean dynamic sustainable efficiency of cropland (D-ESUA) for each region of the Yellow–Huai–Hai River Basin (YHHRB).</p>
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<p>Thematic diagrams of ESUA grade classification.</p>
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<p>Migration of the center of gravity of the variables of sustainable use efficiency of arable land and arable land use pattern.</p>
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<p>ESUA trends by province in the YHHRB.</p>
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<p>Temporal evolution of input–output in the YHHRB.</p>
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21 pages, 4121 KiB  
Article
Provincial Coal Flow Efficiency of China Quantified by Three-Stage Data-Envelopment Analysis
by Gaopeng Jiang, Rui Jin, Cuijie Lu, Menglong Gao and Jie Li
Sustainability 2024, 16(11), 4414; https://doi.org/10.3390/su16114414 - 23 May 2024
Viewed by 1153
Abstract
The exploration of regional variations in coal flow efficiency (CFE) in China and the collaborative strategies for emission reduction are vital for accelerating the progress of ecological civilization within the coal industry and achieving an optimal allocation of coal resources. To unveil the [...] Read more.
The exploration of regional variations in coal flow efficiency (CFE) in China and the collaborative strategies for emission reduction are vital for accelerating the progress of ecological civilization within the coal industry and achieving an optimal allocation of coal resources. To unveil the evolutionary traits of actual CFE and its decomposition, this study employs a current technology based on a combined super-efficient measure (SBM), global SBM, the stochastic frontier approach (SFA), and the global Malmquist–Luenberger index (GML) model on panel data from 2010 to 2021 across 30 provinces in China. The research conclusions are as follows. First, significant efficiency gaps are observed among provinces, showcasing superior performance in the north and east regions. Moreover, the impact of environmental factors and random disruptions on individual slack variables varies, resulting in a decrease of 0.18 and 0.43 in the CFE of source-area and sink-area when these factors are not taken into account. Thirdly, a clear distinction emerges between the technical efficiency change index (EC) and the best-practice gap change index (BPC). Lastly, the CFE displays regional disparities marked by an upward trajectory and fluctuating patterns resembling a “W” shape. Full article
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<p>Global coal reserves and China’s energy consumption structure.</p>
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<p>Three-stage SBM research framework of CFE.</p>
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<p>Spatial–temporal distributions of coal flow function in China.</p>
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<p>Coal flow efficiency (<b>a</b>). Initial efficiency of source-area (<b>b</b>). Initial efficiency of sink-area.</p>
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<p>Coal flow efficiency (<b>a</b>). Actual efficiency of source-area. (<b>b</b>) Actual efficiency of sink-area.</p>
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<p>Comparison efficiency between stage I and stage III (<b>a</b>) source-area and (<b>b</b>) sink-area.</p>
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<p>Trends in efficiency changes in sink-area.</p>
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<p>Trends in GML index decomposition indicators for (<b>a</b>) source-area and (<b>b</b>) sink-area.</p>
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<p>Sink-area regional GML index and decomposition.</p>
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22 pages, 3707 KiB  
Article
A Sustainable Development Study on Innovation Factor Allocation Efficiency and Spatial Correlation Based on Regions along the Belt and Road in China
by Panpan Liu, Guanghui Han, Haichao Yang and Xiaobo Li
Sustainability 2024, 16(7), 2990; https://doi.org/10.3390/su16072990 - 3 Apr 2024
Cited by 2 | Viewed by 1117
Abstract
The level of development of the innovation factor plays a crucial role in supporting the high-quality sustainable development of China’s economy. In order to advance the sustainable development of regional innovation factor allocation efficiency along the Belt and Road this study introduces the [...] Read more.
The level of development of the innovation factor plays a crucial role in supporting the high-quality sustainable development of China’s economy. In order to advance the sustainable development of regional innovation factor allocation efficiency along the Belt and Road this study introduces the super-efficient slacks-based measure (SBM)-data envelopment analysis (DEA)-Malmquist model for static and dynamic analyses of innovation factor allocation efficiency in 17 provinces along the Belt and Road from 2012 to 2021. This study used the Moran index model to analyze spatial correlation. The results show the following: (1) The overall innovation factor allocation efficiency along the Belt and Road is not high, and there are obvious differences among different regions. The eastern region’s efficiency is the highest compared to other regions. (2) According to the efficiency decomposition results, pure technical efficiency (PTE) is the main reason for the low innovation factor allocation efficiency. (3) Through the Malmquist index and decomposition index, it was found that pure technical efficiency (PECH) and scale efficiency (SECH) are key factors in improving technical efficiency (TECH). (4) The analysis of spatial correlation revealed a strong spatial agglomeration feature among the provinces along the Belt and Road. Innovation factor allocation efficiency is mainly manifested in the third quadrant. Finally, drawing on the results of the analysis, suggestions and policies are put forward to improve innovation factor allocation efficiency in the regions along the Belt and Road. This study is of great significance for promoting the sustainable development of the regional innovation level along the Belt and Road in China. Full article
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<p>Map of regional divisions along the Belt and Road.</p>
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<p>Average innovation factor allocation efficiency in regions along the Belt and Road.</p>
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<p>Results of the decomposition of the innovation factor allocation efficiency and trends in change.</p>
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<p>Decomposition results of innovation factor allocation efficiency in regions along the route.</p>
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<p>Trends in Malmquist and decomposition indices.</p>
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<p>Changing trend of Moran index.</p>
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<p>Distribution of Local Moran index quadrantal results.</p>
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17 pages, 2408 KiB  
Article
Efficiency Evaluation of Forest Carbon Sinks: A Case Study of Russia
by Arsenii Vilkov and Gang Tian
Forests 2024, 15(4), 649; https://doi.org/10.3390/f15040649 - 2 Apr 2024
Cited by 4 | Viewed by 1419
Abstract
Forest carbon sinks in Russia are an integral part of the national “Low-carbon development strategy”. However, the influence of natural disasters and various land use policies in economic regions (ERs) raises the issue of forest carbon sink efficiency (FCSE). This study adopted a [...] Read more.
Forest carbon sinks in Russia are an integral part of the national “Low-carbon development strategy”. However, the influence of natural disasters and various land use policies in economic regions (ERs) raises the issue of forest carbon sink efficiency (FCSE). This study adopted a DEA-SBM model that considers undesirable outputs to measure FCSE, and the Malmquist index (MI) approach to analyze total factor productivity (TFP) of forest carbon sinks, using panel data from 2009 to 2021. The results show that the average FCSE was 0.788, with an improvement rate of 21.2%. Scale efficiency is the main factor constraining FCSE in Russia. In twelve ERs, forest carbon sinks are efficient only in the Kaliningrad and West Siberian ERs. In general, forest carbon sinks in Russia are inefficient mainly due to forest fires and other natural disturbances (82.33%); excessive logging activities (38.64%); and lack of carbon absorption capacity (31.70%). The average score of their TFP is 0.970, indicating a decline of 3% over the study period. This is primarily attributed to the decline of 1.6% in technological change. The productivity of forest carbon sinks remained static only in the Kaliningrad ER, while other economic regions performed deterioration trends. Therefore, Russia should enhance the efficiency of forest carbon sinks. Full article
(This article belongs to the Section Forest Economics, Policy, and Social Science)
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<p>Average trend of forest carbon sink efficiency in Russia from 2009 to 2021.</p>
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<p>Total factor productivity and efficiency changes of forest carbon sinks in Russia from 2009 to 2021.</p>
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<p>Average total factor productivity and efficiency changes of forest carbon sinks among economic regions in Russia from 2009 to 2021.</p>
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<p>Average technical efficiency changes and their decomposition values in forest carbon sinks among economic regions of Russia from 2009 to 2021.</p>
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21 pages, 2918 KiB  
Article
Assessing Safety Efficiency in China’s Provincial Construction Industry: Trends, Influences, and Implications
by Xinping Wang, Boxi Zhao and Chang Su
Buildings 2024, 14(4), 893; https://doi.org/10.3390/buildings14040893 - 26 Mar 2024
Cited by 1 | Viewed by 1098
Abstract
Ensuring safety is crucial for promoting the sustainable growth of the construction industry. Assessing safety efficiency is of significant importance for optimizing safety management processes and improving the safety environment. However, the current mainstream methods for evaluating safety efficiency have limitations such as [...] Read more.
Ensuring safety is crucial for promoting the sustainable growth of the construction industry. Assessing safety efficiency is of significant importance for optimizing safety management processes and improving the safety environment. However, the current mainstream methods for evaluating safety efficiency have limitations such as ignoring non-desired outputs and slack variables, the efficiency values being limited to the (0, 1) range, and a narrow perspective. To address these shortcomings, this study focuses on the characteristics of the construction industry and introduces the Super-SBM model and Malmquist index into the assessment of safety efficiency in the construction industry. The study analyzes the evolution characteristics of safety efficiency from both static and dynamic perspectives. Furthermore, using panel quantile regression models, the study identifies the factors influencing safety efficiency and analyzes their heterogeneity. Analyzing panel data from 30 provinces in China from 2015 to 2021, the results show that the overall safety efficiency of the construction industry in China is relatively low, with noticeable spatial clustering characteristics. Provinces in the eastern and central regions exhibit higher levels of construction safety efficiency. The Malmquist index demonstrates a declining trend, with technical efficiency being the primary factor limiting the improvement of safety efficiency in construction. Factors such as per capita GDP, urbanization rate, committed contract amounts, and the number of professionals engaged in survey and design, as well as engineering supervision, have an impact on construction safety efficiency, and the effects of these variables vary across different quantile levels of safety efficiency. This research can assist decision-makers in gaining a better understanding of the safety conditions in different regions of the construction industry. It can also assist in developing customized policies to enhance the health and safety environment, thereby promoting the stable development of the construction industry. Full article
(This article belongs to the Section Construction Management, and Computers & Digitization)
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<p>Main framework of this study.</p>
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<p>Construction safety efficiency of provinces in 2015–2021.</p>
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<p>Spatial distribution of safety efficiency level in 2015.</p>
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<p>Spatial distribution of safety efficiency level in 2018.</p>
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<p>Spatial distribution of safety efficiency level in 2021.</p>
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<p>The safety efficiency change trend from 2015 to 2021.</p>
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<p>Change in quantile regression coefficient of each influencing factor. (<b>a</b>) Per capita GDP; (<b>b</b>) urbanization rate; (<b>c</b>) contract value of construction industry; (<b>d</b>) labor productivity of construction industry; (<b>e</b>) technical equipment rate of construction industry; (<b>f</b>) number of employees in exploration and design institutions; (<b>g</b>) number of employees in engineering supervision institutions. Note: In figure, the horizontal axis represents distinct quantile points of safety efficiency, while the vertical axis signifies the regression coefficient of each variable. The dashed line segment represents the estimated value of explanatory variables through OLS regression, and the area between the two dashed lines depicts the confidence interval of the OLS regression value (confidence level: 0.95). The solid line illustrates the quantile regression estimation result for each explanatory variable, and the shaded portion represents the confidence interval of the quantile regression estimation result (confidence level: 0.95).</p>
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<p>Change in quantile regression coefficient of each influencing factor. (<b>a</b>) Per capita GDP; (<b>b</b>) urbanization rate; (<b>c</b>) contract value of construction industry; (<b>d</b>) labor productivity of construction industry; (<b>e</b>) technical equipment rate of construction industry; (<b>f</b>) number of employees in exploration and design institutions; (<b>g</b>) number of employees in engineering supervision institutions. Note: In figure, the horizontal axis represents distinct quantile points of safety efficiency, while the vertical axis signifies the regression coefficient of each variable. The dashed line segment represents the estimated value of explanatory variables through OLS regression, and the area between the two dashed lines depicts the confidence interval of the OLS regression value (confidence level: 0.95). The solid line illustrates the quantile regression estimation result for each explanatory variable, and the shaded portion represents the confidence interval of the quantile regression estimation result (confidence level: 0.95).</p>
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18 pages, 4693 KiB  
Article
Evaluation of Cropland Utilization Eco-Efficiency and Influencing Factors in Primary Grain-Producing Regions of China
by Jie Li, Zhengchuan Sun, Qin Gao and Yanbin Qi
Agriculture 2024, 14(2), 255; https://doi.org/10.3390/agriculture14020255 - 5 Feb 2024
Cited by 3 | Viewed by 1273
Abstract
Under the backdrop of the “double-carbon” target, the primary grain-producing regions in China are confronted with the tasks of mitigating pollution and carbon emissions and ensuring food security. This paper explores the eco-efficiency of cropland utilization and the factors influencing the primary grain-producing [...] Read more.
Under the backdrop of the “double-carbon” target, the primary grain-producing regions in China are confronted with the tasks of mitigating pollution and carbon emissions and ensuring food security. This paper explores the eco-efficiency of cropland utilization and the factors influencing the primary grain-producing regions in China, utilizing panel data from 13 provinces spanning the period from 2000 to 2019. The analysis employs three models: the super-efficiency SBM model, the Malmquist index model, and the random-effect panel Tobit model. The findings suggest the following: (1) Although the eco-efficiency of cropland utilization in China’s primary grain-producing regions did not reach the production frontier during the period of 2000–2019, it exhibited a high level with an overall upward trend. The limiting factor inhibiting the growth of total factor productivity is lower technical efficiency. (2) There is evident spatial variation in the eco-efficiency of cropland utilization across China, displaying a dynamic evolution from northeast > western > central > eastern to northeast > western > eastern > central. Total factor productivity in each province demonstrates an upward trend, with the east > northeast > west > central ranking. (3) Regarding the influencing factors, the utilization of agricultural production chemicals exerts a negative influence, while the proportion of government financial input, labor input, and irrigation index have a positive impact. Full article
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<p>Flowchart of the method.</p>
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<p>Overview of the research area.</p>
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<p>Trends in eco-efficiency of cropland utilization in China’s primary grain-producing regions, 2000–2019.</p>
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<p>Distribution of eco-efficiency of cropland utilization by provinces in the primary grain-producing regions.</p>
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23 pages, 4618 KiB  
Article
Assessing Eco-Efficiency with Emphasis on Carbon Emissions from Fertilizers and Plastic Film Inputs
by Yixuan Lu, Zhixian Sun, Guanxin Yao and Jing Xu
Agronomy 2023, 13(11), 2720; https://doi.org/10.3390/agronomy13112720 - 28 Oct 2023
Cited by 1 | Viewed by 1491
Abstract
In the context of growing environmental challenges and the push for sustainable agriculture, this study delves into the eco-efficiency of three-season indica rice across 16 key provinces in China from 2004 to 2021. Utilizing the super-efficiency Slacks-Based Measure (SBM) model coupled with the [...] Read more.
In the context of growing environmental challenges and the push for sustainable agriculture, this study delves into the eco-efficiency of three-season indica rice across 16 key provinces in China from 2004 to 2021. Utilizing the super-efficiency Slacks-Based Measure (SBM) model coupled with the Malmquist index, our approach uniquely incorporates undesirable outputs, focusing on carbon emissions from chemical and plastic inputs. Findings indicate that while the overall efficiency hinged around a modest mean, periods like 2005–2006 and 2017–2018 spotlighted the pivotal role of technological advancements and judicious resource use. The Malmquist Index revealed an intricate interplay between technological change and efficiency, notably when accounting for environmental impact. Diverse provincial efficiencies spotlighted the need for bespoke strategies harmonizing efficiency objectives with ecological sustainability. This study emphasizes the indispensable role of technological innovation in advancing eco-efficiency and fostering sustainable agricultural practices, urging for policy changes that prioritize both technology adoption and ecological awareness. Full article
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<p>Study area covering 16 provinces in China.</p>
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<p>Average eco-efficiency of indica rice in China from 2004 to 2021.</p>
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<p>Average eco-efficiency of early indica rice, medium indica rice, and late indica rice in the 16 provinces of China from 2004 to 2021.</p>
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<p>Average eco-efficiency of each province from 2004 to 2021.</p>
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<p>Malmquist Index (MI), Technical Efficiency Change (EC), and Technical Change (TC) for Indica Rice Production across 16 Provinces.</p>
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<p>Change trends of MI, EC, and TC for early indica rice.</p>
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<p>Change trends of MI, EC, and TC for medium indica rice.</p>
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<p>Change trends of MI, EC, and TC for late indica rice.</p>
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<p>Average MI, EC, TC of indica rice in each province of China from 2004 to 2021.</p>
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