Driving Force Analysis of the Agricultural Water Footprint in China Based on the LMDI Method

Zhao, Chunfu; Chen, Bin

doi:10.1021/es503513z

Cited by 157 publications

(68 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, the Log Mean Divisia Index (LMDI) [12][13][14] and Laspeyre [15][16][17][18] are the two most widely used kinds of decomposition algorithms in energy consumption studies. These two algorithms reflect changes in energy consumption by different quantified factors.…”

Section: Introductionmentioning

confidence: 99%

Empirical Research on China’s Carbon Productivity Decomposition Model Based on Multi-Dimensional Factors

Fan

Meng

2015

Energies

View full text Add to dashboard Cite

Based on the international community's analysis of the present CO2 emissions situation, a Log Mean Divisia Index (LMDI) decomposition model is proposed in this paper, aiming to reflect the decomposition of carbon productivity. The model is designed by analyzing the factors that affect carbon productivity. China's contribution to carbon productivity is analyzed from the dimensions of influencing factors, regional structure and industrial structure. It comes to the conclusions that: (a) economic output, the provincial carbon productivity and energy structure are the most influential factors, which are consistent with China's current actual policy; (b) the distribution patterns of economic output, carbon productivity and energy structure in different regions have nothing to do with the Chinese traditional sense of the regional economic development patterns; (c) considering the regional protectionism, regional actual situation need to be considered at the same time; (d) in the study of the industrial structure, the contribution value of industry is the most prominent factor for China's carbon productivity, while the industrial restructuring has not been done well enough.

show abstract

Section: Introductionmentioning

confidence: 99%

Empirical Research on China’s Carbon Productivity Decomposition Model Based on Multi-Dimensional Factors

Fan

Meng

2015

Energies

View full text Add to dashboard Cite

show abstract

“…This is in contrast to the situation in China. For the whole country, population is a positive driving force for increases in the agricultural WF [22]. The rapid population growth in China has led to increased demand for crops, which has directly led to an increase in the WF.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Influencing Factors of Water Footprint Based on the STIRPAT Model: Evidence from the Beijing Agricultural Sector

Chen

Huang

et al. 2016

Water

View full text Add to dashboard Cite

Abstract:Beijing suffers from a severe water shortage. To find the key factors that impact the agricultural water footprint (WF) within Beijing to relieve the pressure on water resources, this study quantifies the agricultural WF within Beijing from 1980 to 2012 and examines the factors of population, urbanization level, GDP per capita, Engel coefficient, and total rural power using an extended stochastic impact by regression on population, affluence and technology (STIRPAT) model. Ridge regression is employed to fit the extended STIRPAT model. The empirical results reveal that the Engel coefficient, which is defined as the total amount of food expenses accounted for the proportion of total personal consumption expenditures, has the largest positive impact on the increase in the agricultural WF, followed by urbanization. In contrast, total rural power, population, and GDP per capita can decrease the agricultural WF. Finally, policy recommendations from technological development, agriculture plantation structure adjustment, and virtual water imports are provided to cope with water shortages.

show abstract

“…It can be used to recognize products that have more influence on the water resource system and provide a way of water saving [12]. The main difference between the concepts of WF and VW is that the former is defined from the perspective of consumption and the latter is defined from the perspective of production [13].…”

Section: Introductionmentioning

confidence: 99%

“…The bottom-up approach based on detailed process information has been widely used to analyze the VW of products and WF of one process or within a certain geographical region [8,[15][16][17][18]. However, this approach fails to distinguish intermediate water use from final water use [13]. Conversely, the top-down approach moves from the highest level to the lower level according to the boundaries of a defined system and subsystem [7].…”

Section: Introductionmentioning

confidence: 99%

“…The I-O model initially developed by Leontief [19], a top-down technique, has been extensively applied to assess VW and WF, as well as sectoral water use on a regional or national scale [20][21][22][23][24][25][26][27][28][29]. Due to the high level of aggregation, the I-O technique is not adept at providing detailed calculation of a specific technique or product [13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Combination of Assessment Indicators for Policy Support on Water Scarcity and Pollution Mitigation

Yang

Song

Higano

et al. 2016

Water

View full text Add to dashboard Cite

Given increasing concern about seeking solutions to water scarcity and pollution (WSP), this paper is intent on developing significant assessment indicators as decision variables for providing reference for policy proposals on the mitigation of WSP. An indicator package consisting of footprints of freshwater consumption (FC) and water pollutant discharge (WPD), virtual contents of freshwater and water pollutants, and inter-sectoral linkages in terms of industrial production, FC and WPD has been newly set up based on an extended input-output model. These indicators allow to provide specific and well-structured analysis on FC, WPD and the economy as well as their implicated interrelationships. The Source Region of Liao River located in northeastern China was selected as an empirical study area to apply the indicator package. The results indicate that farming and production of electricity industries are major contributors to FC; farming and breeding industries, and households are major contributors to WPD. The study area exports a large amount of net virtual total nitrogen, total phosphorus and chemical oxygen demand (29.01ˆ10 3 t, 4.66ˆ10 3 t, 60.38ˆ10 3 t, respectively). Farming and breeding industries are the sectors whose production could be constrained to contribute to mitigating WSP without excessive negative impacts on the economy. Two categories of policies have been proposed to mitigate WSP based on the analysis of the indicator package. One is to introduce direct water pollutant treatment and water-saving policies to the target sectors; the other is to adjust industrial structure. The integrated indicator package developed and the methodology presented are expected to provide policy researchers and decision makers with references for more sound water management.

show abstract

Driving Force Analysis of the Agricultural Water Footprint in China Based on the LMDI Method

Cited by 157 publications

References 67 publications

Empirical Research on China’s Carbon Productivity Decomposition Model Based on Multi-Dimensional Factors

Empirical Research on China’s Carbon Productivity Decomposition Model Based on Multi-Dimensional Factors

Analysis of Influencing Factors of Water Footprint Based on the STIRPAT Model: Evidence from the Beijing Agricultural Sector

Combination of Assessment Indicators for Policy Support on Water Scarcity and Pollution Mitigation

Contact Info

Product

Resources

About