Spatial Dependency of Eco-Efficiency of Agriculture in São Paulo

Peña, Carlos Rosano; Almeida, Carlo Aleksandr Rosano de; Rodrigues, Evaldo Cesar Cavalcante; Serrano, André Luiz Marques

doi:10.15728/bbr.2020.17.3.5

Cited by 10 publications

(5 citation statements)

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“…Studies on AEE have been applied mainly at the national [ 10 , 11 ], provincial [ 12 ], specific regional [ 13 ], city [ 14 , 15 ] and community levels [ 16 ]. The DEA has become the most common evaluation method for AEE due to its capability to determine the efficiency of multi-input and multi-output systems and effectively handle non-parametric boundaries [ 17 , 18 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Re-measurement and influencing factors of agricultural eco-efficiency under the ‘dual carbon’ target in China

Yang,

Guan,

Chen

et al. 2024

Heliyon

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Section: Literature Reviewmentioning

confidence: 99%

Re-measurement and influencing factors of agricultural eco-efficiency under the ‘dual carbon’ target in China

Yang,

Guan,

Chen

et al. 2024

Heliyon

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“…Allocatively efficient DMUs are DMUs that manage to produce more efficiently than all the others, operating on the ideal scale for the production process. They also tend to have the highest profit among all their peers [18,25,[28][29][30].…”

Section: Theoretical Frameworkmentioning

confidence: 99%

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

Saiki,

Serrano,

Rodrigues

et al. 2024

Preprint

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The exponential growth of the agricultural industry in the Amazon region has brought about notable economic advancements. However, this growth has substantially cost the region’s ecosystems, manifesting in increased deforestation and biodiversity degradation within the Amazon forest. This article is dedicated to assessing the eco-efficiency of agricultural production in Amazon biome municipalities. It places particular emphasis on identifying critical determinants through the utilization of the classic Data Envelopment Analysis (DEA) model for efficiency computation, super-efficiency models for distinctive characterization, bootstrap computational techniques for robust resampling, and the Malmquist index for calculating annual eco-efficiency indices of each Decision-Making Unit (DMU). An exploration of the correlation between efficiency and meteorological attributes of the municipalities is conducted. The findings of this study reveal several significant points: 1) Eco-efficient municipalities within the Amazon Biome can serve as benchmarks for other DMUs striving to attain optimal input-output levels. 2) Most municipalities in the Amazon Biome operate closely to the productive frontier due to the prevalent technology employed in their agricultural activities. 2) The nature of the technological frontier’s return suggests that small and large DMUs possess eco-efficiency potential. 4) The current dataset does not yield conclusive evidence regarding a direct correlation between the variables. Leveraging this information, strategic pathways can be formulated to drive economic development in tandem with the sustainability of Amazon Biome municipalities. These strategies promise to foster social, economic, and environmental benefits for the populace while providing valuable insights to inform future research within this thematic domain.

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“…Agricultural eco-efficiency (AEE) carries the inherent connotation of eco-efficiency and embodies the ecological and economic aspects of agricultural sustainability, which means producing more quantity and higher quality agricultural products and services, exploiting fewer natural resources, enhancing resource utilization, and causing minimal adverse environmental impacts (Picazo-Tadeo et al, 2011;Coluccia et al, 2020;Gołaś et al, 2020). The measurement of AEE can effectively quantify agricultural sustainability performance, which has been applied at different scales, from the national level (Pang et al, 2016;Gancone et al, 2017), provincial level (Xu et al, 2020;Liao et al, 2021), city level (Rosano-Peña et al, 2020;Yang et al, 2021), village level (Xiang et al, 2020), to the farm level (Barnes and Thomson, 2014;Stępień et al, 2021). However, our review of the previous related studies indicates that there is a lack of research on evaluation of AEE at the county level, especially in Jiangsu province.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Evolution and Improvement Potential of Agricultural Eco-efficiency in Jiangsu Province

Jin

2021

Front. Energy Res.

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This study evaluated the agricultural eco-efficiency (AEE) of 77 counties and districts in Jiangsu Province from 1999 to 2018 using the slack-based measure (SBM) of efficiency in data envelopment analysis (DEA) (SBM-DEA) and analyzed its spatiotemporal evolution characteristics and influencing factors. We found that 1) the overall AEE, pure technology efficiency (PTE), and scale efficiency (SE) exhibited a fluctuating downward trend. AEE exhibited a significantly positive spatial association and an increasingly widening regional inequality. 2) AEE featured the “high south” and “low north” spatial pattern, with the high-value regions concentrated around the Taihu Lake plain region in southern Jiangsu Province (Sunan) and low-value regions scattered across most of the northern Jiangsu Province (Subei) cities. The high-high and low-low spatial association types further confirmed the existence of the north–south agglomeration pattern. 3) PTE and SE exhibited a similar “high south” and “low north” spatial pattern to that of AEE. The areas with the growth trends of AEE, PTE, and SE were clustered in Xuzhou and Nanjing city and in the bordering regions between Yangzhou and the Huai’an city, and also between Changzhou and the Wuxi city. 4) Excessive redundant input and use of pesticides, chemical fertilizers, agricultural diesel, labor, land, and agricultural carbon emissions, all have been the primary factors affecting Jiangsu’s AEE. Irrigation also considerably affected AEE, while mechanical power and agricultural film have minimal effects. The majority of counties and districts in the Subei, central Jiangsu Province (Suzhong), and Ningzhen Yang Hilly region experienced excessive usage of chemical fertilizers, pesticides, chemical fertilizers, agricultural diesel, labor, and land. The findings can improve understanding of the spatial association effect and underlying impediment of AEE and can further help policymakers promoting agricultural eco-efficiency.

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Spatial Dependency of Eco-Efficiency of Agriculture in São Paulo

Cited by 10 publications

References 0 publications

Re-measurement and influencing factors of agricultural eco-efficiency under the ‘dual carbon’ target in China

Re-measurement and influencing factors of agricultural eco-efficiency under the ‘dual carbon’ target in China

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

Spatiotemporal Evolution and Improvement Potential of Agricultural Eco-efficiency in Jiangsu Province

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