Green development performance of water resources and its economic-related determinants

Abstract

In the context of severe water shortage and pollution, enhancing the green total-factor productivity of water resources (GTFPWR) is critical for the green development of water resources. This study aims to propose a novel two-stage analytical framework, in which the GTFPWR is measured in the first stage and its economic-related determinants are examined in the second stage. In this two-stage analytical framework, we improve and integrate four classical methods, namely, the undesirablesuper-slack-based measure, global Malmquist-Luenberger productivity index, system generalized method of moments and fixed-effects panel threshold models. The proposed analytical framework is capable of addressing four practical issues simultaneously: equal efficiency, undesirable outputs, impact scenarios and endogenous biases. We can thus more accurately and comprehensively evaluate the GTFPWR and its determinants. To validate the applicability and suitability of the proposed methodology, we collected the panel data about water resources across 30 provinces in China from 2005 to 2015 for an empirical study. The main findings of this study are as follows: i) the level of water utilization is a critical factor for the government to decide whether to increase the GTFPWR via foreign direct investment and trade; ii) the amount of wastewater should be effectively reduced to

Evaluation of Green Development Efficiency of the Major Cities in Gansu Province, China

“…In China, the positive drivers of GD include corporate regulatory compliance, environmental administrative decentralization [1], land-related policies [53], socioeconomic elements [11], urbanization and technological innovation [30], trading of carbon emission [9], invention patents and green technology patents [54], and development of improved water resource management techniques [3]. On the other hand, the factors that hinder GD include corporate ownership structure, dependence on foreign investment, fiscal policy [30], and the discharge of wastewater into lakes and rivers leading to severe water pollution across the majority of provinces in China [3]. Nevertheless, the government of China remains a strong advocate of GD.…”

“…The output variables include desired and non-desired outputs. The desired (i.e., good) output was represented by the gross domestic product (GDP) indicator and the non-desired (i.e., bad) output was represented by total SO 2 emission [1,21], industrial wastewater emission [3,4], and industrial smoke. According to the unguided super-efficient SBM model [1,74], the GD level of the major cities in Gansu province from 2010 to 2017 was estimated using MaxDEA software.…”

“…Green development (GD) is a new model of sustainable development that centers on social, economic, and environmental stewardship to promote human wellbeing through efficient natural resource utilization that fosters the provision of ecosystem services [1,2]. These ecosystem services include improved air and water quality for human survival [3,4], enhanced biodiversity in towns and cities [5], and resource-use efficiency [6,7]. Since the outbreak of the international financial crisis in 2008, the United Nations have been advocating for a "Green New Deal".…”

“…Developing countries such as Cambodia and South Africa have also developed strategic plans to attain green economy status [16,17]. Furthermore, recent evidence has shown that less utilization of water and the production of less waste from water is an essential indicator of GD [3]. Other studies show that GD is positively related to GDP [18], the efficiency of energy utilization [4], and negatively correlated to the emission of Sulphur dioxide, which depends on compliance with government regulation [1].…”

How is China’s energy security affected by exogenous shocks? Evidence of China–US trade dispute and COVID-19 pandemic

“…Regarding future research directions, we will extend this work to consider the iron ore supply chain, especially using the public data of the Australian mining industry [ 57 ]. Besides, we will implement operations research (i.e., Mixed Integer Programming), machine learning and game theory techniques as analytical tools to deal with quantitative energy security management problems [ 58 – 61 ].…”