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Abstract Industry and agriculture in Malaysia are the main contributors to economic growth and employment. These sectors also play an important role in Malaysia's total exports. The question then is whether technological innovation, sectoral output, and exports growth have had a real impact on these two sectors, which are very important for policy-making. This paper attempts to empirically identify such relations using econometric methods, including an autoregressive distributed lag (ARDL) bounds testing method and a dynamic ordinary least squares (DOLS) during 1978–2018. The results confirmed that overall long-run economic growth is the main contributor to the increase in energy consumption with a greater magnitude than in the short-run. In the long-run, an increase of 1% in economic growth leads to an increase of 4.6% and 1.1% in energy demand in agriculture and industrial sectors, respectively. Exports are the second largest contributor to energy demand in the overall economy and the agriculture sector. Finally, the technological innovation that enhances energy efficiency is only effective in reducing energy consumption in the industrial sector, which ultimately reduces emissions.

The construction sector continues to experience significant challenges brought by new techniques and technologies. Hence, there is a dire need for construction companies to address critical issues concerning changing environmental conditions, construction innovations, market globalization and many other aspects, thereby enhancing their competitive edge. Thus, the primary goal for this research is to develop a multi-criteria decision making model that would consider and evaluate all essential factors in determining the competitiveness index of construction companies. In the developed model, three new pillars (3P) for competitiveness are introduced: (1) non-financial internal pillar; (2) non-financial external pillar; and (3) financial pillar. The 3P includes 6 categories and 26 factors that are defined and incorporated in the developed assessment model for the purpose of measuring the companies’ competitiveness. The weights for the identified factors are computed using fuzzy analytical network process (FANP) to diminish the uncertainty inherited within the judgment of the respondents. The weight of factors and their affiliated performance scores are used as an input for the preference ranking organization method for enrichment evaluation (PROMETHEE II) technique. In this regard, PROMETHEE II is undertaken as a ranking technique to prioritize any given construction company by determining its respective competitiveness index. The developed model is validated through five cases studies that reveal its potential of illustrating detailed analysis with respect to the competitive ability of construction companies. A sensitivity analysis is carried out to determine the most influential factors that affect the competitiveness of construction companies. It is anticipated that the developed evaluation model can be used in the decision-making process by all parties involved in construction projects. For instance, contractors can leverage the evaluation model in taking better decisions pertinent to the markup values. In addition, it can benefit employers in the evaluation process of contractors.

This study aimed to identify a viable solution for the development of China’s electric power industry in line with “dual carbon” objectives. Accordingly, we collected and analyzed 2230 policy documents spanning 25 years to track the Chinese government’s focus on the electric power sector over time using latent Dirichlet allocation topic modeling. Our results reveal that the government’s area of emphasis differs across different stages of development. By analyzing the evolution of policy implementation, we identified the actions taken by government agencies at the policy level to promote the electric power industry. We then distilled the key themes of government attention and challenges facing the green transition of electricity in the context of “dual carbon”. Based on this analysis, we propose practical recommendations for restructuring power energy, enhancing power security, and improving power market efficiency. These findings hold important implications for China to achieve an environmentally sustainable electric power transformation.

This study investigates 30 provinces in China between 2003 and 2014. Kernel density method is used to analyse the dynamic evolution of haze pollution and technological innovation research and development (R&D), while spatial econometric analysis is used to study the impact of technological innovation on the haze pollution. The results show that haze pollution presents global spatial autocorrelation and local spatial cluster in China. China’s haze pollution has a significant spatial dependence and spatial spillover. A disproportion distribution pattern of haze pollution exists among provinces in China: the central region is the most polluted area followed by the western region, the northeast region and the eastern region. The kernel density curve shows that the gap between technology innovations R&D among provinces has expanded year by year. There is a polarization between the technological innovations R&D. Dynamic evolution results showed that during 2003–2012, the kernel density distribution curve of haze pollution showed a leftward shift, indicating that provincial haze pollution decreased gradually. However, the kernel density distribution curve of haze pollution showed a rightward shift in 2014, and the provincial haze pollution increased. During 2003–2012, the gap of haze pollution among different provinces in China gradually narrowed, while in 2014, the gap increased significantly. Spatial econometric results show that the indirect effects and the total effects of technological innovation are significantly negative. Technological innovation can not only reduce the regional haze pollution but also indirectly lead to the decline in the haze pollution of adjacent provinces through the knowledge spillover effect. The increase of population density can effectively reduce the haze pollution. There is an inverted ‘U type’ relationship between economic development and haze pollution. The increase of traffic pressure will aggravate the degree of haze pollution.

Abstract Artificial Intelligence (AI) is becoming the engine of a new round of technological revolution and industrial transformation; as such, it has attracted much attention of scholars in recent years. Surprisingly, scarce studies have shed lights on the effects of AI on the environment, especially with respect to carbon intensity. Based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, we use Chinese industrial sector data from 2005 to 2016 to investigate how AI affects carbon intensity. The empirical results show that AI, as measured separately by the adoption of robotics by industry and the number of academic AI-related papers, significantly reduces carbon intensity. The results remain robust after addressing endogenous issues. We find that there are both stages and industrial heterogeneity in the effects of AI on carbon intensity. AI had a more decrease effect on carbon intensity during the 12th Five-Year Plan than the 11th. Compared with capital-intensive industries, AI tends to have a more decrease effect on carbon intensity in the labor-intensive and tech-intensive industries. To enlarge the effects of AI on reducing carbon intensity, the government should promote the development and application of AI and implement differentiated policies in line with the industry characteristics.

Abstract This paper examines the relationship between hydroelectricity consumption and economic growth in China, while controlling for fossil fuel consumption, financial development, capital, institutional quality and globalization and its components for the period, 1970–2014. We have employed the Bayer and Hanck, (2013) combined cointegration test to examine the long-run relationships between those variables as well as the autoregressive distributed lag method with structural breaks as a robustness check. The empirical findings demonstrate a long-run relationship between those variables. Hydroelectricity consumption, fossil fuel consumption, capital, financial development and globalization and its components have a positive influence on GDP in China. The findings also provide predominant evidence on the long-run feedback hypothesis between the variables. The findings suggest that policies should be implemented to increase the role hydropower in the energy mix for sustainable economic growth in the country.

Facing informal environment regulation carried out by the environmental protection organizations, we study and judge its inhibitory effect on air pollution and the acting path. Based on panel data of 285 cities in China from 1998 to 2018, a time-varying difference-in-difference model is used to estimate the effect of informal environment regulation on air pollution. The estimation results show that informal environment regulation can inhibit air pollution significantly under different scenarios. Green technology innovation is introduced into the research and a mediating effect model is used to investigate the influencing mechanism. Informal environment regulation strengthens pressure on pollutant emissions. This forces enterprises to enhance the investment and application of green technology innovation during production. Mechanism analysis shows that informal environment regulation inhibits air pollution by encouraging the application of green technology innovation. The above conclusions are still valid after a series of robustness tests, including parallel trend, placebo test and instrumental variables. The research conclusions provide empirical evidence for the construction of a diversified air-pollution control system and demonstrate the practical significance of informal environment regulation to improve air quality.

This article is motivated by a conundrum: How can shale gas development be encouraged and managed without complete knowledge of the associated risks? To answer this question, we used back propagation (BP) neural networks and expert scoring to quantify the relative risks of shale gas development across 12 provinces in China. The results show that the model performs well with high predictive accuracy. Shale gas development risks in the provinces of Sichuan, Chongqing, Shaanxi, Hubei, and Jiangsu are relatively high (0.4~0.6), while risks in the provinces of Xinjiang, Guizhou, Yunnan, Anhui, Hunan, Inner Mongolia, and Shanxi are even higher (0.6~1). We make several recommendations based on our findings. First, the Chinese government should promote shale gas development in Sichuan, Chongqing, Shaanxi, Hubei, and Jiangsu Provinces, while considering environmental, health, and safety risks by using demonstration zones to test new technologies and tailor China’s regulatory structures to each province. Second, China’s extremely complex geological conditions and resource depths prevent direct application of North American technologies and techniques. We recommend using a risk analysis prioritization method, such as BP neural networks, so that policymakers can quantify the relative risks posed by shale gas development to optimize the allocation of resources, technology and infrastructure development to minimize resource, economic, technical, and environmental risks. Third, other shale gas industry developments emphasize the challenges of including the many parties with different, often conflicting expectations. Government and enterprises must collaboratively collect and share information, develop risk assessments, and consider risk management alternatives to support science-based decision-making with the diverse parties.