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Tourist-related activities continuously increase the competition for water, food, land, natural resources and the associated energy consumption. The input of per capita energy consumption boosts economic growth which also increases the greenhouse effect and global warming. While extensive research efforts have been devoted to exploring the future growth of tourism-related emissions, little attention has been given to evaluating the dynamic linkages among tourism development, CO2 emissions, and economic growth in a region where tourism is a pillar industry. This exploratory study selects Hainan Island, China, where tourism significantly contributes to the economic growth of the Island as an example. Data were obtained from the official statistics during the period 1986 to 2014. During that period of time period, Hainan has seen a faster growth in foreign and domestic tourism. The study aims at validating the environmental Kuznets curve (EKC) hypothesis and investigating the relationship between tourism-related economic growth and CO2 emissions by applying a bivariate vector autoregressive (VAR) model. The results of this study showed that there is no direct evidence to support the inverted-U relationship between tourism-related economic growth and CO2 emissions in this Island area. The specific implications for future research are also discussed. [ABSTRACT FROM AUTHOR]

Challenges brought on by the rapid rate of economic growth include the depletion of natural resources (NAR) and environmental deterioration. Due to its abundant mineral resources and rapid economic growth, China has gained attention from both the developing and developed worlds. Using data from 1970/Q1 to 2020/Q4, this paper evaluates the time‐frequency nexus between CO2 emissions and natural resources, coal consumption, and economic growth. The study employs the Wavelet Local Multiple Correlation (WLMC) to explore this nexus. This approach enhances the capacity to comprehend the fundamental relationships between these indicators at different frequencies. To the best of the investigator' knowledge, this is the first empirical investigation to explore the time‐frequency nexus between natural resource, economic growth, coal use, and CO2 emissions in China, thus filling a gap in the literature. Furthermore, wavelet coherence is employed as a robustness check. The WLMC bivariate cases revealed that economic growth, coal consumption, and natural resources drive CO2 emissions positively at all frequencies, that is, in the short and long‐term, thus leading to environmental degradation. Furthermore, the wavelet coherence results support the outcomes of the WLMC bivariate cases. This study offers significant implications for policy and insights that are supported by the findings. [ABSTRACT FROM AUTHOR]

This study investigates whether natural gas exploitation is a blessing or a curse for provincial and prefecture economic growth. This study employs regression models and synthetic control methods (SCM) to investigate the research question based on hybrid panel and time-series data from 1993 to 2015 from 14 prefecture cities in Sichuan province in China. Based on the provincial data of Sichuan, the regression results show that natural gas exploitation is a curse for the provincial economic development of Sichuan because of the negative associations between gas resources and economic performance, for which the increasing rent-seeking in the province may partially account. However, the SCM results find that the local economy of Dazhou city may benefit from its sufficient natural gas resources in the short run, but it is not sustainable. Meanwhile, the gas resource has no significant contributions to the regional economic growth of Sichuan overall, which is partially attributed to the fact that resource exploitation has no significant spillover effects on some sustainable contributors, such as education, innovation, and others. The practical implication for the economic governors is that the resource-consumed economic route is not sustainable, although it may drive local economic growth in the short run. [ABSTRACT FROM AUTHOR]

Traditional GDP calculations exclude the ecological environment and natural resource depletion, arguing that sustained economic growth can lead to sustained welfare improvements. The Index of Sustainable Economic Welfare (ISEW) is a monetary measure of sustainability and economic welfare aimed at overcoming some of the limitations of GDP. Given the significant impact of industrial development, the demographic composition, and the balance of payments in economic growth and sustainable economic well-being, we propose an improved ISEW method (SE-ISEW), where the structural index is used instead of the Atkinson index as a measure of structural imbalance. This study calculates and compares the ISEW and SE-ISEW for 30 provinces in mainland China from 1997 to 2017 and discusses highly controversial methodological assumptions related to income inequality, long-term environmental damage, and nonrenewable energy consumption. Findings show that China's GDP is larger than ISEW and SE-ISEW respectively. Moreover, the "relative threshold effect"—increases in social welfare are slower than the expansion of economic scale—has been found in many provinces. In the case of rapid economic growth, the gap between GDP and ISEW or SE-ISEW has increased over time. This study reveals an unbalanced development of China's economic growth and sustainable welfare growth and provides a new way of thinking about structural transformation. [ABSTRACT FROM AUTHOR]

Malthusian population theory states that, when population growth exceeds the growth of the means of production, socioeconomic development will be hindered. This paper analyzes this issue from the perspective of uncoordinated urbanization. We used the data available for 262 cities at the prefecture level and above in China, for the years 2000, 2005, 2010, and 2015. We empirically investigated the relationship between uncoordinated urbanization and urban economic growth. We also examined how this relationship is affected by natural resource constraints. The research results show that there are temporal and spatial differences in the distribution of uncoordinated urbanization in China. From a temporal perspective, from 2000 to 2015, 45.61% of China's 262 cities show under‐urbanization, while 36.45% of cities show over‐urbanization. However, the number of cities with over‐urbanization is growing. From a spatial perspective, the number of cities in the northeastern region with over‐urbanization is declining. Conversely, the number of cities with over‐urbanization in Beijing in the central and southeastern coastal areas is increasing. Empirical research shows that uncoordinated urbanization and urban economic growth have an inverted U‐shaped relationship. Abundant natural resources can make the inverted U‐shaped curve of uncoordinated urbanization and economic growth flatter. The "resource curse" seems to occur only under high levels of uncoordinated urbanization (over‐urbanization). For some cities where there is under‐urbanization, abundant natural resources are conducive to urban economic growth. [ABSTRACT FROM AUTHOR]

China's breakneck economic growth has been accompanied by an expanding development finance agenda. Many have hypothesized that China is undermining the West's drive to promote good governance globally, and in Africa in particular, by predominantly distributing money to poorly governed countries. This paper explores whether the determinants of Chinese development finance in Africa differ from those of Western countries. It finds that Western countries send more development finance than China to better governed African countries—those with lower corruption levels, better democratic outcomes, and a better human rights track record (though only the latter two have a negative relationship with Chinese development finance in absolute terms). This paper also finds that bilateral trade and UN voting alignment have a stronger impact on China's development finance than that of Western countries and that China allocates more development finance than the West to richer and more resource-dependent African countries. [ABSTRACT FROM AUTHOR]

China has been undergoing rapid economic growth since its reform and opening-up at the expense of resource depletion and environmental pollution. We estimated the monetary costs of resource depletion and environmental pollution in China over the past 14 recent years by establishing integrated mathematical models with System for Integrated Environmental and Economic Accounting (SEEA) framework. Results showed four points. Firstly, the total temporal costs of natural resource depletion and environmental pollution witnessed an inverted 'U' shape pattern with the turning point in 2012. The relationship between cost of mineral resource depletion, as well as water pollution cost, and per capita GDP were found to follow the Inverted-U shape environmental Kuznets curve (EKC). Secondly, the average annual share of natural resources of the total costs was more than 80%, indicating that the rapid economic growth of China is highly dependent on resource depletion. Thirdly, Although China still has the highest worldwide in its total energy consumption and CO 2 emission, China's struggles with adjustment of industrial & energy consumption structure, and pollution controls have resulted in a general temporal decline of the share of resource and environmental costs to China's GDP. The green GDP of China is growing at an average rate of 14.76%. Finally, although in the process of manufactured goods for the whole world, China has reduced environmental pollution and left it at home as much as possible. Driving forces behind the progress in green GDP of China were analyzed by using Differences-in-Differences method and Spearman rank correlation analysis. [Display omitted] • An integrated model was proposed to calculate China's costs of resources and environment. • Total costs of resource depletion and environment witnessed an inverted 'U' shape. • Driving forces behind decreasing trend of green GDP in China were analyzed. [ABSTRACT FROM AUTHOR]