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

Abstract

China's water scarcity problems have become more severe because of the unprecedented economic development and population explosion. Considering agriculture's large share of water consumption, obtaining a clear understanding of Chinese agricultural consumptive water use plays a key role in addressing China's water resource stress and providing appropriate water mitigation policies. We account for the Chinese agricultural water footprint from 1990 to 2009 based on bottom up approach. Then, the underlying driving forces are decomposed into diet structure effect, efficiency effect, economic activity effect, and population effect, and analyzed by applying a log-mean Divisia index (LMDI) model. The results reveal that the Chinese agricultural water footprint has risen from the 94.1 Gm3 in 1990 to 141 Gm3 in 2009. The economic activity effect is the largest positive contributor to promoting the water footprint growth, followed by the population effect and diet structure effect. Although water efficiency improvement as a significant negative effect has reduced overall water footprint, the water footprint decline from water efficiency improvement cannot compensate for the huge increase from the three positive driving factors. The combination of water efficiency improvement and dietary structure adjustment is the most effective approach for controlling the Chinese agricultural water footprint's further growth.

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

“…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.…”

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

“…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.…”

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

“…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].…”

“…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].…”

“…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].…”