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While economic development-driven anthropogenic emissions pose challenges to ecological sustainability, the international travel and tourism sector has appeared as a hot contestant to bring sustainability to the ecological systems across varying development levels. This work investigates the diversified effects of the international travel and tourism sector and economic development on ecological deterioration, in the presence of urban agglomeration and energy use efficiency, across the development levels of China's 30 provincial units from 2002 to 2019. It contributes in two ways. (i) The stochastic estimation of environmental impacts by regression on population, affluence, and technology (STIRPAT) is modified to integrate the variables like international travel and tourism sector, urban agglomeration, and energy use efficiency. (ii) We measured an international travel and tourism sector index (ITTI) and made use of a continuously updated bias correction strategy (CUBCS) and a continuously updated fully modified strategy (CUFMS) for the long-term estimations. Besides, we used the bootstrapping-based causality technique for determining causality directions. The core results are as follows: Firstly, ITTI and economic development produced an inverse U-type association with ecological deterioration for the aggregate panels. Secondly, provinces exhibited a diverse range of links in that ITTI mitigated (boosted) the ecological deterioration in eleven (fourteen) provinces presenting diversified shapes of linkages. Economic development established the environmental Kuznets curve (EKC) theory with ecological deterioration in merely four provinces; however, the non-EKC theory is verified in twenty-four divisions. Thirdly, in China's east zone (high development scale), the ITTI revealed the ecological deterioration reduction (promotion) impact in eight (two) provinces. China's central zone (moderate development scale) exhibited ecological deterioration promotion in half of the provinces, and the other half showed a reduction impact. In China's west zone (low development scale), it promoted ecological deterioration in eight provinces. Economic development promoted (reduced) ecological deterioration in a single (nine) province(s). In China's central zone, it boosted (mitigated) the ecological deterioration in five (three) provinces. In China's west zone, it promoted (reduced) ecological deterioration in eight (two) provinces. Fourthly, urban agglomeration and energy use efficiency deteriorated and improved the environmental quality in aggregated panels, respectively; however, a diverse range of effects are observed for provinces. Finally, a unilateral bootstrap causality, from ITTI (economic development) to ecological deterioration, is revealed in twenty-four (fifteen) provinces. A bilateral causality is established in a single (thirteen) province(s). Based on empirical findings, policies are suggested., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Environmental sustainability investigation has been a hotly debated topic of the modern literature; however, past studies have primarily overlooked its transmissibility or spillover outreach across economies. Herein, we investigate the novel aspect of whether transportation-based environmental taxation spatially induces spillover impacts across Italy, Germany, and France over the 1994-2020 period by employing a simultaneous spatial equation with multi-country dynamic stochastic general equilibrium modeling (DSGEM) framework. Transportation-based environmental taxation of the domestic economy negatively impacts its own investment and consumption, while it impacts the economy of neighboring economies positively. Change in output and investment in the domestic economy can be well explained by the environmental volatility of the domestic economy, whereas the environmental volatility of neighboring economies does not contribute much to explain the change in investment and output of the domestic economy. Volatility in pollution discharge occurs more by environmental volatility in the neighboring economy than in the domestic economy, and validating that environmental sustainability is transmissible across regions and economies. It urgently calls for environmental protection policies integrated and coordinated across the countries and regions to spread and capitalize on environmentally and economically favorable and sustainable effects globally. Achieving the spatially transmitted positive environmental and economic outcomes would help strengthen the Sustainable Development Goals (SDG), with a particular focus on Climate Action (SDG13), Sustainable Production and Consumption (SDG12), and Affordable and Sustainable Energy for All (SDG7)., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

China seems ambitious to achieve a "carbon emissions peak" before 2030 and "carbon neutrality" before 2060. To deal with this emissions mitigation plan, technology innovations are regarded as a crucial factor. However, considering its rebound effect (CO 2 emissions driving effect) through economic growth, technology innovations might not prove a promising contributor to CO 2 reduction. Therefore, there is a need to investigate further the nexus between technology innovations and CO 2 emissions for conclusive debate. Based on the data of 215 cities in China, this paper uses mediating effects model to investigate the direct and indirect impacts (through economic growth and industrial structure transformation) of technology innovations on CO 2 emissions from a microeconomic perspective. The main results suggest that technology innovations generally increase CO 2 emissions in China both directly and indirectly. The impact of technology innovations and economic growth on CO 2 emissions indicated the EKC characteristics. Furthermore, the contributions of technology innovations to CO 2 emissions are distinguished in different regions. Thus, there is an urgent need for China to promote innovations in "clean technology" and to transform industrial structure to the tertiary industry to achieve the targets of carbon neutrality and emissions peaking., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Energy security, job development, and minimal environmental damage are just a few of the reasons why green energy technologies have gained popularity. Still, developing these technologies in underdeveloped countries is sluggish due to several bottlenecks. To find and prioritize the bottlenecks to development of green energy technologies (GETs), this study has used the case of South Africa. In this study, technical, financial, social, regulatory, geographical, and institutional bottlenecks are six headings under which 22 bottlenecks are further subdivided. For prioritizing these bottlenecks, a Multi-Objective Optimization based on Ratio Analysis (MOORA) strategy combined with an Evaluation based on Distance from Average Solution (EDAS) method was used. The top three bottlenecks impacting the GETs' development involved social, institutional, and regulatory, whereas in sub-headings, corrupt political systems, knowledge of the public, and fixed investments were revealed to be the most significant hurdles. In addition to that, a framework is also developed for assessing how each bottleneck hinders the development of green energy technologies. The results of this study recommend that formulation of policy using the bottom-up approach is important. Notably, politicians at the highest levels should work together to bolster government agencies so that they can monitor and carry out the policies designed to advance GETs. Building the institutional capacity of government offices is crucial for eliminating corruption in political offices and progressing GETs in South Africa., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Since renewable energy has the paramount importance of achieving environmental sustainability, developing countries like Pakistan have been facing numerous renewable energy generation (REG) development challenges. Thus, the REG development in any country can be attributed to its crucial drivers as well as crucial barriers. This work aims to identify crucial influence factors of REG development by conducting multi-facet content analysis as well as partial least square-based structural equation modeling analysis on data collected through a questionnaire survey. It has been conducted based on academicians from four Pakistani universities as well as professionals from Islamabad Electric Supply Company. The key outcomes are as follows: First, through multi-facet content analysis, ten crucial influence factors are identified. Among those factors, lack of good governance, renewable energy adaptation, and governmental energy policies are revealed to be the crucial barriers to REG development, whereas endowed resources, power production approach, renewable energy demand, investment environment for renewable energy projects, economic returns of renewable energy projects, environmental effects, and public acceptance are found to be the crucial drivers of REG development. Based on relative significance, the lack of good governance is unveiled to be the most significant factor, while environmental effects is found to be the least significant one. Moreover, among the drivers of REG development, public acceptance is found to be the prime contributor. At the same time, among crucial barriers, lack of good governance has shown the largest contribution to REG development., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Since preceding several decades, the carbon emissions based standard Environmental Kuznets Curve (EKC) has been tested and supported by a plethora of studies in countries around the globe. The current study estimated the inward foreign direct investment (IFDI)-augmented EKCs for China's 27 provincial divisions employing the advanced econometric methodologies involving cross-sectional dependence, slope heterogeneity, and second generation-based estimation procedures. The study has further contributed through a modification to "Stochastic Influence by Regression on Population, Affluence, and Technology" (STIRPAT) in terms of including IFDI to the standard model. Accordingly, this work estimated the standard EKC (involving economic development-carbon emissions linkage) as well as IFDI-carbon emissions linkage within the STIRPAT framework, by employing a panel vector error-correction-based estimation procedure. The findings revealed that (1) the conventional EKC estimates for national and regional samples (i.e., aggregate samples) presented linkages differing from the EKC links for the provincial divisions. It suggested that the EKC at the aggregated levels is likely the consequence of aggregation bias problem. (2) The links between IFDI (in power and non-power sector) and carbon emissions provided inverse U shape for the aggregate samples, while the provincial divisions presented heterogeneous results. This is perhaps because of the aggregation bias. Hence, the aggregation bias puzzle is unriddled. (3) Also, heterogeneous patterns are found in terms of turning points, degree of impact, and nature of the association of income and IFDI with carbon emissions. The meaningful policies can be extracted for the large countries encompassing varied economic development levels, such as China, if the EKC is evaluated at the disaggregate scales., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.)

This research investigates the dynamic interactive associations among sustainable investment in the energy sector, air pollution, and sustainable development. To this end, it employs a "one-step" system-generalized method of moments (GMM) and "one-step" differential-GMM estimators, covering the period between 1996 and 2017. In this context, it utilizes the simultaneous equations of the dynamic panel data model for panel data of 27 Chinese provinces and municipalities. We have developed a new model of sustainable development, which incorporates sustainable investment in the energy sector and air pollution to offer a robust theoretical foundation for considering the underlying relations. The system-GMM estimator is used for the full data set; however, differential-GMM is utilized for the subsets of data, in order to tackle the small sample bias problem. The empirical outcomes provide several vital insights in that they yield mixed findings for the aggregated sample and subsets of data. For example, a two-way causal relationship occurs for all the panels, except the central part (medium development regions), between sustainable investment in the energy sector and sustainable development. Contrary to this, causality runs from air pollution to sustainable investment in the energy sector in a full data set and the central part (medium dev.). Nevertheless, the opposite is true in the case of the eastern part (most developed regions) of China. Still, the same relationship runs in either direction in the case of the western part (least developed regions). On the other way around, the feedback hypothesis of causality is confirmed, across all the samples, between air pollution and sustainable development. Hence, sustainable development and air pollution are overwhelmingly interdependent, in the country as well as the province and municipality level of the Chinese economy.

This study investigates the heterogeneous causal linkages between urbanization, the intensity of electric power consumption, water-based pollutant emissions, and GRP in regional China by developing an urbanization-augmented "Stochastic Impacts by Regression on Population, Affluence, and Technology" (STIRPAT) model. A whole country panel of 29 provinces as well as region sub-panels of China, for the period 1999 to 2018, are estimated employing common correlated effects mean group approach (CCEMGA), which offers robustness against heterogeneous characteristics and cross-sectionally dependent series. From the theoretic modeling aspect, the intensity of electric power consumption and urbanization have been introduced as the determinants of water-based pollutant emissions in the STIRPAT modeling framework. Based on empirical results, first, GRP growth has shown appealing behavior in the form of its heterogeneous impacts on water-based pollutant emissions growth in the case of different regions. For instance, its impact is noted to be positive and statistically significant for the western region, which turned positive but statistically insignificant for the intermediate region. And it further turned significantly negative in the case of the eastern region. We call this phenomenon as "development level-based emission mitigation effect." Second, in terms of the impact of GRP growth on urbanization, the "development-based urbanization ladder effect" has been found. Based on heterogeneous causal links, firstly, the existence of a positive bilateral causal link between the intensity of electric power consumption and GRP growth and urbanization and GRP growth has been validated. Secondly, a positive unidirectional causal link emerged from urbanization to the intensity of electric power consumption and water-based pollutant emissions growth. Thirdly, the causal connection between GRP growth and water-based pollutant emissions growth remained very interesting and of mixed nature. Based on empirical findings, useful policies are extended. Graphical abstract.

This work investigates the dynamic heterogeneous causal links among financial development, construction industry, energy use, and environmental quality across the development levels, for 30 Chinese provinces during the period 2001-2016. For this purpose, a model of environmental quality has been constructed introducing the financial development and construction industry as endogenous factors. A Pedroni's cointegration is employed and found the long-run cointegrating mechanism among the variables of interest. The dynamic common correlated effects mean group approach (DCCEMGA) is adopted to estimate the impact elasticities. Moreover, for robustness check, a sensitivity analysis is conducted employing common correlated effects mean group approach (CCEMGA). The main results are first, a two-way positive causal bridge is existent between gross domestic product (GDP) and energy use, construction industry and GDP, and financial development and GDP. In terms of the contribution of the construction industry to economic performance, construction industry-driven growth acceleration impact is observed. Second, a one-way positive causal link is identified running from financial development (both the banking sector and stock market) to the construction industry and is termed as finance-driven construction promotion effect. Also, a one-sided positive causal connection is operating from financial development and construction industry to energy use and carbon dioxide emissions. Finally, a standard environmental Kuznets curve (EKC), financial development-augmented EKC, and construction industry-augmented EKC hypotheses are found valid in the whole country and eastern region of China. Based on empirics, a regional heterogeneity has been observed in terms of the degree of impact and statistical significance while comparing the regional panels. The sensitivity analysis proved the empirical results to be robust and reliable. Moreover, based on the findings, policy recommendations are documented. Graphical abstract.

Massive investments in energy industry may either promote or mitigate the air pollution which is likely to influence the economic performance. In light of this, the current work is an empirical examination of heterogeneous dynamic causal interactions among energy investment, sulphur dioxide (SO 2 ) emissions and economic growth in regional China. A modified STIRPAT model is developed to introduce energy investment as a determinant of technology. Based on a modified model, a simultaneous equations system has been established to examine the three-way causal interactions among the variables of interest. As a first step, an error-correction-based second generation Westerlund (Oxford Bulletin of Economics and Statistics 69: 0305-9049, (2007). cointegration has been employed and found the long-run relationship. The simultaneous equations have been estimated by employing second generation dynamic common correlated effects mean group estimator (DCCEMGE) for 30 Chinese provinces and cities from 2001 to 2017. The empirical analysis has revealed that the energy investment induces emissions promotion effect while economic growth introduces emissions curtailment effect. However, the emissions curtailment effect remained slightly stronger than emissions promotion effect both at country and regional levels. Further, the energy investment growth uncovered economic performance enhancement effect. Furthermore, the SO 2 emissions growth induced economic performance deterioration effect. In this regard, the economic performance deterioration effect exceeded the economic performance enhancement effect. Besides, there is a bidirectional causality operative between SO 2 emissions and gross regional product (GRP) growth for the country as well as regional samples. On the contrary, a positive bilateral causal relationship between energy investment and GRP growth is found for the country as well as regional samples. Similarly, it has been found that there exists a positive bilateral causal association between SO 2 emissions and energy investment. Graphical abstract.

This paper is a maiden empirical attempt to analyze the dynamic causal linkages among urban agglomeration, electricity consumption, construction industry, and economic performance, making use of simultaneous structural equations. A national panel of 30 provinces and three sub-national panels of China, for time span 2000 to 2016, have been estimated employing system and difference generalized method of moments (GMM) estimator. A construction industry-augmented model of economic growth has been proposed, incorporating construction industry and urban agglomeration as exogenous shocks to the aggregate production and electricity consumption as the input of production function. The core empirical results are first, the urban agglomeration and construction industry positively cause electricity consumption but are not caused by the same. It revealed the critical role played by urban agglomeration along with construction industry in boosting electricity consumption. Second, the economic performance positively causes electricity consumption, urban agglomeration, and construction industry and is also caused by the same. Third, urban agglomeration causes the construction industry and is caused by the same. It exposed the mutual role of urban agglomeration and urban industry in reinforcing each other in the times of high economic performance. Finally, among the three regions, the eastern zone is found to be strongest in terms of linkages among urban agglomeration, construction industry, electricity consumption, and economic performance. The intermediate zone is moderately strong, while the western zone is found to have the least strong linkages as compared with the two regions. These results are in line with the sub-national level of economic development of China. These findings, in terms of statistical significance, are highly robust across all the panels. Furthermore, depending upon empirical results, the related strategies are proposed.