Estimation of realistic renewable and non-renewable energy use targets for livestock production systems utilising an artificial neural network method: A step towards livestock sustainability

Elahi, Ehsan; Cui, Weijun; Jha, Sunil Kumar; Zhang, Huiming

doi:10.1016/j.energy.2019.06.084

Cited by 99 publications

(53 citation statements)

References 54 publications

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“…2 The global environmental problem has increased with increase in energy demand. 3,4 China is currently the largest energy producer and consumer in the world. 5 Energy consumption and CO 2 emissions have been at top level in the world for many years.…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal pattern evolution and influencing factors of energy–environmental efficiency: A case study of Yangtze River urban agglomeration in China

Zhong

Peng

Elahi

2020

Energy & Environment

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Improving energy–environmental efficiency is prerequisite for sustainable development. In order to explore ways to improve energy–environmental efficiency, this paper uses the undesired output slack-based model to measure the energy–environmental efficiency of the Yangtze River urban agglomeration based on the input and output index data from 2008 to 2017, and its spatial and temporal pattern evolution is analyzed by using kernel density estimation, Gini coefficient, and coefficient of variation. Moreover, the Tobit regression model is used to analyze the influencing factors of the energy–environmental efficiency of the Yangtze River urban agglomeration. The results indicate that the energy–environmental efficiency of each city is increased continuously, and the regional differences are gradually narrowed. The spatial pattern is changed from polar nucleus type to valley type, and finally the distribution characteristics of “overall high” are formed. Overall, the energy–environmental efficiency presents a spatial layout of “high in the east and low in the west.” The regression results show that the level of economic development and energy–environmental efficiency are “U-type” associated characteristics, and government regulation and population density have significant positive effects on it. Industrial structure and technological progress have negative effects on it, and the effect of opening degree is not significant.

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Section: Introductionmentioning

confidence: 99%

Spatial and temporal pattern evolution and influencing factors of energy–environmental efficiency: A case study of Yangtze River urban agglomeration in China

Zhong

Peng

Elahi

2020

Energy & Environment

Self Cite

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“…With urban economic growth and population expansion, series of ecological and environmental problems such as water pollution, land occupation, soil degradation, air pollution, and reduction of biodiversity have become more and more obvious (Dai et al, 2016;Wu et al, 2019Wu et al, , 2020a. Environmental pollutions have already caused the health and safety of Chinese residents (Ebenstein et al, 2015;Elahi et al, 2019a;Gu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

The Impacts of Urban Manufacturing Agglomeration on the Quality of Water Ecological Environment Downstream of the Three Gorges Dam

Huang

You

2021

Front. Ecol. Evol.

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As an important way to promote economic growth and to improve ecological environment quality, industrial agglomeration strategy has been widely accepted and implemented in various regions of China. In theory, industrial agglomeration is conducive to reducing pollution emissions. However, this needs to be tested in reality. The purpose of this paper is to test the impacts of manufacturing industry agglomeration on the quality of water ecological environment in 17 prefecture-level cities downstream of the Three Gorges Dam. Therefore, a theoretical model was established to test the direct and indirect effects. Empirically, results show that the direct effect of manufacturing agglomeration on the quality of water ecological environment is −0.426, which provides statistical evidence for the important role of manufacturing agglomeration in improving the quality of water ecological environment. The indirect effect of manufacturing agglomeration on ammonia nitrogen (NH3-N) is 0.118% through technological innovation, the indirect effect on NH3-N is 0.114% through economic growth, and the indirect effect on NH3-N is 0.254% through industrial structure. Due to the low overall level of the manufacturing industry and its reliance on dirty technologies, the indirect effects of industrial agglomeration are not conducive to improving the quality of the water ecological environment. Therefore, it is necessary to establish a long-term mechanism to promote the evolution of the regional economy along a path that is conducive to the improvement of the water ecological environment.

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“…The question of how to accurately identify the critical areas of ecological protection has become the primary problem to be solved. Recently, various studies have focused on agriculture (Elahi et al, 2018a;Elahi et al, 2019aElahi et al, 2021a;Elahi et al, 2021b) and livestock sectors (Elahi et al, 2017;Elahi et al, 2018b;Elahi et al, 2019b) for estimation of resources and institutional barriers in the land use. Moreover, some studies focused on environmental issues (Peng et al, 2020;Zhong et al, 2020); however, identification of the critical area of ecosystem service supply and demand at different scales has not been determined yet.…”

Section: Introductionmentioning

confidence: 99%

Identifying Critical Area of Ecosystem Service Supply and Demand at Different Scales Based on Spatial Heterogeneity Assessment and SOFM Neural Network

Liao

Gao

et al. 2021

Front. Environ. Sci.

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The purpose of this study was to establish a spatial multi-scale integrated assessment framework for critical areas of ecosystem service supply and demand, in order to provide theoretical support for regional ecological protection planning and refined management. Taking the typical hilly area of the upper reaches of the Yangtze River in China as an example, based on the assessment matrix of land use and ecosystem services, we used the method of spatial heterogeneity assessment and self-organizing feature mapping (SOFM) to explore the identification and regionalization of critical areas of ecosystem services at regional and small scales. The results show that there was spatial heterogeneity and scale effects of ecosystem services under the two scales. The small-scale supply–demand pattern was greatly affected by microtopography and land use patterns, and the importance of ecosystem services was as follows: forest area in the upper part of the mountain > orchard and dry area in the middle and lower part of the mountain > valley farming area > flat town and farming area. The regional-scale supply–demand pattern was greatly affected by landscape structure, location conditions and social economy, and the importance of ecosystem services was as follows: south > west > north > central. The SOFM network quantitatively identified four types of ecological areas with clear dominant functions at regional and small scales. The balance between supply and demand in the studied ecosystem service areas was I < II < III < IV, in which IV was the critical area in terms of supply and I was the critical area in terms of demand. This assessment framework can improve the spatial accuracy and objectivity of the quantification and mapping of ecosystem services, and provide new ideas for multi-scale identification and expression of ecosystem services.

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Estimation of realistic renewable and non-renewable energy use targets for livestock production systems utilising an artificial neural network method: A step towards livestock sustainability

Cited by 99 publications

References 54 publications

Spatial and temporal pattern evolution and influencing factors of energy–environmental efficiency: A case study of Yangtze River urban agglomeration in China

Spatial and temporal pattern evolution and influencing factors of energy–environmental efficiency: A case study of Yangtze River urban agglomeration in China

The Impacts of Urban Manufacturing Agglomeration on the Quality of Water Ecological Environment Downstream of the Three Gorges Dam

Identifying Critical Area of Ecosystem Service Supply and Demand at Different Scales Based on Spatial Heterogeneity Assessment and SOFM Neural Network

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