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      Characteristics and prediction of agricultural ecological efficiency for coordination between economy and environment

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          Abstract

          Agricultural ecological efficiency is an important tool with which to measure the coordination of the sustainable development of agricultural economies and ecological environments. In this paper, a super-efficiency slacks-based measures model was used to measure the agricultural ecological efficiency in Hebei Province. The characteristics of spatial and temporal evolution patterns were explored using a spatial Markov transfer matrix. The results showed that (i) based on measurements, the agricultural ecological efficiency in Hebei Province showed regional differences in four regions (eastern, northern, central and southern Hebei) and 141 counties; (ii) from the perspective of evolutionary characteristics of agricultural ecological efficiency, the overall development of in Hebei Province was good, with more concentrated spatial distribution and more obvious direction, while the type of transfer of agricultural ecological efficiency in Hebei Province showed strong stability that was significantly affected by geographical neighborhood conditions and the club convergence phenomenon; (iii) from the perspective of the long-term evolutionary trend of agricultural ecological efficiency, the areas adjacent to counties with low efficiency had limited potential for improvement, and the areas adjacent to counties with high grade had great potential. However, it was difficult to achieve large-scale improvement in agricultural ecological efficiency in Hebei Province, whether the impact of geospatial backgrounds was considered or not.

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          A slacks-based measure of super-efficiency in data envelopment analysis

          Kaoru Tone (2002)
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            Enhancing Digital Innovation for the Sustainable Transformation of Manufacturing Industry: A Pressure-State-Response System Framework to Perceptions of Digital Green Innovation and Its Performance for Green and Intelligent Manufacturing

            Low carbon and digitalization are the general trends of manufacturing upgrading and transformation. Digital technology enables the whole process of green manufacturing and breaks down the spatial barrier. To achieve the dual carbon goals, the pressure-state-response (PSR) model, in which digital technology enables the green innovation of the manufacturing industry, was theoretically analyzed in this study. The measurement system of the digital green innovation (DGI) in the manufacturing industry was constructed according to the PSR framework. An evaluation model based on the analytic hierarchy process and the deviation maximization technique for order preference by similarity to an ideal solution method was constructed to measure the level of DGI. The results of this study from Chinese manufacturing are as follows. (i) The measurement system of the level of DGI in manufacturing industry includes a pressure system, state system and response system. (ii) In the past five years, the comprehensive index of the DGI in manufacturing industry has generally shown a trend of fluctuating rise. There are overall low and unbalanced phenomena in all regions. The gap decreased from 0.1320 to 0.1187, showing a gradually narrowing trend. (iii) Compared with other regions, the composite index of DGI is generally higher in the regions with a better ecological environment in the east and a more developed economy in the north. State parameters are higher than pressure and response parameters in most areas. (iv) Compared with other regions, the composite index of DGI in western and southern regions is lower, and the parameters of pressure, status and response are basically coordinated. (v) The application degree of digital technology, the emission intensity of waste water/exhaust gas of output value of one hundred million yuan and the expenditure intensity of digital technology adopted by enterprises are the key influencing factors of DGI in the manufacturing industry. This study not only proposed an evaluation index system of the digital green innovation level, but also puts forward policy guidance and practical guidance of digital technology to accelerate the green and intelligent manufacturing industry.
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              Farm household efficiency in Bangladesh: a comparison of stochastic frontier and DEA methods

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: Project administrationRole: Resources
                Role: Formal analysisRole: Funding acquisitionRole: SupervisionRole: Validation
                Role: InvestigationRole: SupervisionRole: Validation
                Role: Data curationRole: ResourcesRole: SoftwareRole: Validation
                Role: ResourcesRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Project administrationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                30 May 2024
                2024
                : 19
                : 5
                : e0302971
                Affiliations
                [1 ] College of Land and Resources, Hebei Agricultural University, Baoding, China
                [2 ] College of Urban and Rural Construction, Hebei Agricultural University, Baoding, China
                [3 ] College of Geography and Tourism, Baoding University, Baoding, China
                [4 ] College of Economics and Management, Hebei Agricultural University, Baoding, China
                East China Normal University, CHINA
                Author notes

                Competing Interests: The authors declare no conflict of interest.

                Author information
                https://orcid.org/0000-0001-6885-7412
                Article
                PONE-D-23-09145
                10.1371/journal.pone.0302971
                11139313
                38814941
                22ce1747-3bcd-44f4-b414-65464c2f5245
                © 2024 Ma et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 27 March 2023
                : 14 April 2024
                Page count
                Figures: 6, Tables: 7, Pages: 22
                Funding
                Funded by: Hebei Provincial Social Science Foundation Project
                Award ID: HB22YJ012
                Award Recipient :
                This research was funded by Hebei Provincial Social Science Foundation Project (HB22YJ012, awarded to LX). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                The data are owned by a third-party organization, Hebei Agricultural University. The access to this research data is restricted by the data access committee of Hebei Agricultural University with the document reference number (gtxy2023003). The data presented in this study are available on request from Hebei Agricultural University (E-mail: gtxl@ 123456hebau.edu.cn ).

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