As the basis of regional development, water-energy-food (W-E-F) is usually a bottleneck too. China’s strategy of sustainable development has been threatened and challenged by the spatially unbalanced distribution and the insufficient total amount of the water-energy-food supply, and the unbalanced socioeconomic development. Each year from 1997 to 2015, three pressure indices (i.e. water pressure index, energy pressure index and food pressure index) were decomposed into three orthotropic vectors by using principal component analysis, and the W-E-F pressure index were then calculated and created by vector synthesis method. For the thirty province units in China (no data are available for Hong Kong, Macao and Taiwan and no energy data for Tibet), after the temporal variations of the water pressure index, the energy pressure index, the food pressure index and the W-E-F pressure index were checked, eight variables (i.e. degree of per capita education, per capita GDP, per capita farmland area, per capita meat production, per capita aquatic product, population density, effective irrigation area, urbanization rate) which related to the W-E-F pressure were employed and three cross-section (i.e. 1997, 2004 and 2015) were chosen to carry out the geographically weighted regression (GWR) analysis. In the three developed GWR models, the dependent variable was the z-score standardized W-E-F pressure index differences between the start year and the end year, i.e. 1997 and 2004 (for model 1), 2004 and 2015 (for model 2), 1997 and 2015 (for model 3), and the eight independent variables were the respective z-score standardized differences (i.e. for the eight employed variables) between the start year and the end year. The factors which influence the variation of W-E-F index can be discovered from the developed GWR models and the conclusions are as follows: 1) The W-E-F pressure index rose initially and then declined from 1997 to 2015. Spatially, the W-E-F pressure decreased progressively from Southeast China to Northwest China, and larger pressure happened in Northeast China and those coastal provinces where urbanization level was higher. 2) The energy pressure index had made more significant contribution to the W-E-F pressure index than the water pressure index and the food pressure index. The energy pressure index was higher in central and east China and the water pressure index was bigger for those provinces in the north of Huaihe river. However, the food pressure index was higher for those coastal provinces in the south of the Yangtze River and for those provinces in west China. 3) In China, the water pressure index rose in most provinces, and the energy pressure index and the food pressure index declined in most provinces from 1997 to 2015. 4) The driving forces of the W-E-F pressure changes were different for the different phases. Overall, the same change direction was observed between most of the variables and the W-E-F pressure index. However, the opposite change direction existed between degree of per capita education and the W-E-F pressure index, so did between per capita GDP and the W-E-F pressure index. The main causes of the increased W-E-F pressure were the increased population density, the changed component of food supply, the conditions of food production and the economic development. The influences of socioeconomic factors on the W-E-F pressure rose progressively from 1997 to 2015. The effectual ways to reduce the W-E-F pressure were to improve the degree of per capita education and to reshape the economic development.
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Major changes are occurring with far reaching implications for the existing equilibria or disequilibria in the water-energy-food-environment interface. The increased demand of energy worldwide will reflect directly and indirectly on water-dependent systems. Direct implications will come from higher energy prices, which make extraction and conveyance of water more costly. Indirect implications will be in the form of demand for alternative energy sources. It triggers demand for hydropower and remains a major driver驴along with some environmental policies驴for biofuel expansion. The key question is how these effects may alter water allocation and influence food security, rural poverty and environmental sustainability. This paper sets the background and context of this special issue by highlighting some of the major water-related policy issues related to the subject and provides an overview and synthesis of the papers in this special issue. Besides offering insight into how these papers address these questions in the practical context of few selected countries and basins, this paper also indicates some key areas for future research on the subject
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The areas of energy, water and food policy have numerous interwoven concerns ranging from ensuring access to services, to environmental impacts to price volatility. These issues manifest in very different ways in each of the three “spheres”, but often the impacts are closely related. Identifying these interrelationships a priori is of great importance to help target synergies and avoid potential tensions. Systems thinking is required to address such a wide swath of possible topics. This paper briefly describes some of the linkages at a high-level of aggregation – primarily from a developing country perspective – and via case studies, to arrive at some promising directions for addressing the nexus. To that end, we also present the attributes of a modelling framework that specifically addresses the nexus, and can thus serve to inform more effective national policies and regulations. While environmental issues are normally the ‘cohesive principle’ from which the three areas are considered jointly, the enormous inequalities arising from a lack of access suggest that economic and security-related issues may be stronger motivators of change. Finally, consideration of the complex interactions will require new institutional capacity both in industrialised and developing countries.
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Traditional regression techniques such as ordinary least squares (OLS) are often unable to accurately model spatially varying data and may ignore or hide local variations in model coefficients. A relatively new technique, geographically weighted regression (GWR) has been shown to greatly improve model performance compared to OLS in terms of higher R 2 and lower corrected Akaike information criterion (AIC C ). GWR models have the potential to improve reliabilities of the identified relationships by reducing spatial autocorrelations and by accounting for local variations and spatial non-stationarity between dependent and independent variables. In this study, GWR was used to examine the relationship between land cover, rainfall and surface water habitat in 149 sub-catchments in a predominately agricultural region covering 2.6 million ha in southeast Australia. The application of the GWR models revealed that the relationships between land cover, rainfall and surface water habitat display significant spatial non-stationarity. GWR showed improvements over analogous OLS models in terms of higher R 2 and lower AIC C . The increased explanatory power of GWR was confirmed by the results of an approximate likelihood ratio test, which showed statistically significant improvements over analogous OLS models. The models suggest that the amount of surface water area in the landscape is related to anthropogenic drainage practices enhancing runoff to facilitate intensive agriculture and increased plantation forestry. However, with some key variables not present in our analysis, the strength of this relationship could not be qualified. GWR techniques have the potential to serve as a useful tool for environmental research and management across a broad range of scales for the investigation of spatially varying relationships.
2018,
Vol. 38
),张海军
