Integrated water risk early warning framework of the semi-arid transitional zone based on the water environmental carrying capacity (WECC)

Abstract

Water risk early warning systems based on the water environmental carrying capacity (WECC) are powerful and effective tools to guarantee the sustainability of rivers. Existing work on the early warning of WECC has mainly concerned the comprehensive evaluation of the status quo and lacked a quantitative prejudgement and warning of future overload. In addition, existing quantitative methods for short-term early warning have rarely focused on the integrated change trends of the early warning indicators. Given the periodicity of the socioeconomic system, however, the water environmental system also follows a trend of cyclical fluctuations. Thus, it is meaningful to monitor and use this periodicity for the early warning of the WECC. In this study, we first adopted and improved the prosperity index method to develop an integrated water risk early warning framework. We also constructed a forecast model to qualitatively and quantitatively prejudge and warn about the development trends of the water environmental system. We selected the North Canal Basin (an essential connection among the Beijing-Tianjin-Hebei region) in China as a case study and predicted the WECC in 25 water environmental management units of the basin in 2018-2023. We found that the analysis of the prosperity index was helpful in predicting the WECC, to some extent. The result demonstrated that the early warning system provided reliable prediction (root mean square error of 0.0651 and mean absolute error of 0.1418), and the calculation results of the comprehensive early warning index (CEWI) conformed to the actual situation and related research in the river basin. From 2008 to 2023, the WECC of most water environmental management units in the basin had improved but with some spatial differences: the CEWI was generally poor in areas with many human disturbances, while it was relatively good in the upstream regions with higher forest and grass covers as well as in the downstream areas with larger water volume. Finally, through a sensitivity analysis of the indicators, we proposed specific management measures for the sustainability of the water environmental system in the North Canal Basin. Overall, the integrated water risk early warning framework could provide an appropriate method for the water environmental administration department to predict the WECC of the basin in the future.

Environmental Regulation and Industrial Green Transformation in the Yangtze River Economic Zone --- A Perspective Based on Emission Heterogeneity

“…A growing number of studies have shown that the "Porter hypothesis" does exist, and that the implementation of environmental regulation policies can effectively motivate rms to engage in green innovation, resulting in additional bene ts from green innovation and weakening the negative impact of environmental regulation compliance costs [2] . On the other hand, Leeuwen et al in their empirical study of green technology innovation in the Netherlands only con rmed the "weak Porter hypothesis" but could not support the "strong Porter hypothesis" [3] The third view is that the effect of environmental regulation on green economic development is phased and non-linear. According to some studies, the effect of environmental regulation has a certain "time lag" and "non-linear" characteristics.…”

“…The existing academic literature on the impact of environmental regulations on industrial green transformation is relatively small, but there are more studies on the impact of environmental regulations on the level of economic green development and green economic e ciency. 3 According to the relevant research ideas, the existing studies mainly start from two paths: one is to examine the differences in the impact of different types of environmental regulations on economic green development, and the other is to focus on the impact of different environmental regulation intensity on economic green development.…”

“…There are many measurement methods about industrial green transformation in academic circles, which can be summarized into two ideas: the rst idea is to follow the theoretical connotation of industrial green transformation, improve the evaluation index system of industrial green development level, and form an index system that can re ect the dynamic evolution characteristics of industrial green transformation. 3 The second idea is that industrial green total factor productivity is the fundamental driving force of industrial green transformation, which can more accurately re ect the level and dynamic evolution of industrial The second idea is that industrial green total factor productivity is the fundamental driving force of industrial green transformation, which can more accurately re ect the level and dynamic evolution of industrial green transformation, so the stochastic frontier analysis or data envelopment method is used to measure industrial green total factor productivity [8] . Comparing the two ideas, we can see that the rst idea can effectively explain the theoretical connotation of industrial green transformation, but it cannot encompass all the connotations of industrial green transformation, and there is a possibility of overestimation or underestimation due to the lack of relevant index data.…”

“…According to some studies, the effect of environmental regulation has a certain "time lag" and "non-linear" characteristics. 3 Green economic e ciency, industrial green transformation, industrial capacity utilization, green technological innovation, etc. show a "U" shaped trend of decreasing and then increasing under the effect of environmental regulation [4] .…”

“…In order to fully mitigate the estimation bias caused by the problem of reverse causality endogeneity, as well as the problem of multicollinearity and "causal reciprocity" between the core explanatory variables and the explanatory variables, which may be caused by the selection of indicators, this paper draws on the research method of Shiyi Chen33 and incorporates the instrumental variable (V) into the analytical model to construct an instrumental variable-based least squares regression model (IV-2SLS), as follows: (2) lnIGT it =Φ 0 +Φ 1 lnER it +φ2lnControl_α it +ε it (3) In the model, IV denotes the instrumental variable, e it is the random disturbance term, and the other variables are the same as the baseline model.…”