China’s economic development has resulted in significant resource consumption and environmental damage. However, technological progress is important for achieving coordinated economic development and environmental protection. Appropriate environmental regulation policies are also important. Although green total factor productivity, environmental regulations, and technological progress vary by location, few studies have been conducted from a spatial perspective. However, spatial spillover effects should be taken into consideration. This study used energy consumption, the sum of physical capital stock and ecological service value as total capital stock, the number of employed people as inputs, sulfur dioxide emissions as undesired outputs, and green GDP as total output to obtain green TFP through a slacks-based measure (SBM) global Malmquist-Luenberger Index. This study also estimated China’s biased technological progress under environmental constraints from 2004 to 2015 based on relevant data (e.g., green GDP, total capital stock, and employment figures). The relationship between green total factor productivity (GTFP), technological progress, and environmental regulation was then examined using a spatial Durbin model. Results were as follows: (1) Based on the complementary elements, although the labor costs gradually increase, the rapid accumulation of capital leads to technological progress that is biased toward capital. However, technological progress in the labor bias can significantly increase GTFP. (2) There is a u-shaped relationship between existing environmental regulations and GTFP. Technological progress can significantly promote GTFP in the surrounding areas through existing environmental regulations. (3) Under spatial weight, the secondary industry coefficient was negative while human capital stock and FDID had positive effects on GTFP. Technological progress is the source of economic growth. It is therefore necessary to promote biased technological development and improve labor-force skills while implementing effective environmental regulation policies.

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Based on provincial panel data of water footprint and grey water footprint, and with the help of data envelopment analysis model considering and without considering the undesirable output, this paper estimates the water resources utilization efficiency in China from 1997 to 2011. The spatial weighting matrix based on economy-spatial distance function is established to discuss spatial autocorrelation of water resources utilization efficiency. With the help of absolute β-convergence model, this paper concludes that there exists β-convergence in the water resources utilization efficiency. Under the conditions of considering and without considering the undesirable output, it takes about 52.6 and 5.6 years respectively to achieve the extent of half of convergence. By mean of the spatial Durbin econometric model, this paper studies spatial spillover effects of the provincial water resources utilization efficiency in China. The results are as follows. 1) With considering and without considering the undesirable output, there is significant spatial correlation in provincial water resource efficiency in China. 2) Under the two cases, the spatial autoregressive coefficients (ρ) are 0.278 and 0.507 respectively, at 1% significance level. There exist the spatial spillover effects of provincial water resources utilization efficiency. 3) With considering the undesirable output, these factors of the education funds, the transportation infrastructure, and the industrial and agricultural water consumption proportion have positive impacts. These factors of foreign direct investment, the industry value-added water consumption per ten thousand yuan, per capita water consumption, and the total precipitation have negative impacts. 4) Without considering the undesirable output, the factor of GDP per laborer has a greater positive significant influence on the water resources utilization efficiency. However the facts of industry value-added water consumption in ten thousand yuan and the transportation infrastructure have no significant influence. 5) Regardless of undesirable output of water resources utilization efficiency, the assessment of the present real water resources utilization in China will be distorted and policy-making will be misled. The water efficiency measure considering environmental factors (such as gray water footprint) is more reasonable.

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Abstract:The economic development of China's coastal areas is being constrained by resources and the environment, with sustainable development being the key to solving these problems. The data envelopment analysis (DEA) model is widely used to assess sustainable development. However, indicators used in the DEA model are not selected in a scientific and comprehensive manner, which may lead to unrepresentative results. Here, we use the driver-pressure-state-welfare-response (DPSWR) framework to select more scientific and comprehensive indicators for a more accurate analysis of efficiency in China's coastal area. The results show that the efficiencies of most provinces and cities in China's coastal area have a stable trend. In the time dimension, efficiency was rising before 2008, after which it decreased. In the spatial dimension, China's coastal provinces and cities are divided into three categories: high efficiency, low efficiency, and greater changes in efficiency. By combining DPSWR and DEA, we produce reliable values for measuring efficiency, with the benefit of avoiding the incomplete selection of DEA indicators.

Water shortage is a common problem around the world, especially in developing countries. Water shortage is closely linked to natural and social conditions, but the linkages between these natural and social conditions and their underlying temporal and spatial variation are less well explored. This paper details an application of the Driving‐Force‐Pressure‐State‐Impact‐Response (DPSIR) model, a holistic and sustainable tool for resources planning and management, and uses comprehensive weights to evaluate the water poverty (wp) in China from 1997 to 2014. This study applies the Kernel density estimation model to analyze the temporal variation trend and uses the least square error model to analyze the spatial pattern of wp. The results show the level of wp is gradually declining over time and the improvements in the coastal and inland wp situation are not spatially harmonious, and there are four primary types of wp in China based on drivers and causal mechanisms: D‐P‐I, D‐P‐I‐R, D‐P‐S‐I, and D‐P‐S‐I‐R. Furthermore, we analyze the main causes of spatial difference of wp and put forward corresponding countermeasures. The research findings are intended to provide a new insight for the evaluation of wp in the context of sustainable development, breaking past limitations that arise in simplified analyses using a single method, and to provide a strategy for regional water resources management to relieve wp.