This research implements the panel data control method to evaluate the stimulative effects of the “One Belt, One Road” initiative on trade performance in China. We constructed a counterfactual of China’s trade surplus by exploiting the unobservable common factors that create observable trade balances among other countries. We also modified the traditional control group selection by extending it to the Elastic-Net method. This study found the following: (i) China’s annual trade surplus increased sharply by 10.69% on average since 2015. In contrast, analysis of the counterfactual showed that the net exports of China would have remained constant without the stimulation of the “One Belt, One Road” initiative; (ii) These results are robust to exports growth rates and checking by various control group selections; (iii) Although the analysis shows return to an average trading balance, we should not underestimate the benefits of the initiative in the long run.

China has shifted its focus from export-led growth to technology-driven growth. The “One Belt, One Road” initiative (OBOR) is aimed at promoting economic integration with the rest of the world that might boost China’s ability to upgrade its manufacturers. We evaluate the impact of the OBOR initiative on the manufacturing industries of China. In other words, we examine whether the greater openness entailed by the initiative has been a catalyst for upgrading China’s export portfolio. We demonstrate that the proposition has been such a catalyst formally and adduce some empirical support for the proposition by using a panel of the top 80 exporting countries in the world during the period from 2002–2017 to evaluate the impact of the economic integration brought by the OBOR in terms of upgrades to Chinese manufacturers. Meanwhile, when domestic R&D generates knowledge spillover, the benefits of greater openness become partly indeterminate, thus counterfactual analysis was adopted to preclude potential endogeneity. By relating the values of exports at the two-digit harmonized system level to data on sectors to conduct integration within global production networks, we find that the greater openness brought by the OBOR initiative has actively increased manufacturing complexity and facilitated industrial upgrading in China.

In recent years, users have increasingly focused on the privacy of social networking sites (SNS); users have reduced their self-disclosure intention. To attract users, SNS rely on active platforms that collect accurate user information, even though that information is supposed to be private. SNS marketers must understand the key elements for sustainable operation. This study aims to understand the influence of motivation (extrinsic and intrinsic) and self-disclosure on SNS through soft computing theories. First, based on a survey of 1108 users of SNS, this study used a dominance-based rough set approach to determine decision rules for self-disclosure intention on SNS. In addition, based on 11 social networking industry experts’ perspectives, this study validated the influence between the motivation attributes by using Decision-Making Trial and Evaluation Laboratory (DEMATEL). In this paper, the decision rules of users’ self-disclosure preference are presented, and the influences between motivation attributes are graphically depicted as a flow network graph. These findings can assist in addressing real-world decision problems, and can aid SNS marketers in anticipating, evaluating, and acting in accord with the self-disclosure motivations of SNS users. In this paper, practical and research implications are offered.

Green production, which reduces hazardous industrial discharges per unit output, is promoted throughout China. The Environmental Kuznets Curve suggests a negative correlation between hazardous industrial discharge and economic growth due to green innovation. This study expanded the EKC framework by including heterogeneity in evaluating the relationship between hazardous industrial discharges and economic growth to reflect green transformation. Administrative ranking disparity is identified as one of the fundamental driving forces of green production transition in a developing country. Building on an enriched EKC framework, we used a spatial estimation model to exclude spatial effects and obtained accurate estimates of the classified regions. The modified research method was used to examine whether industrial pollution has been reduced in 267 cities and towns in China from 2007 to 2021. Environmental protection performance was examined to estimate whether there is a switch to green manufacturing. As industrial hazards are of different types, the author sought to determine whether there was a decrease in industrial sulphur dioxide emissions, wastewater, solid waste, or dust, even though more industrial hazards were recycled than before. The spatial estimates indicated that (a) the national level of pollution remains positively linked with the total output, and every percentage of output growth increases sulphur dioxide emissions by 444.573 tons; (b) a positive relationship between economic growth and wastewater is altered by environmental protection in cities, while the general decoupling between economic growth and other types of industrial pollution, such as solid waste and industrial dust, was not observed; (c) growth in the southeast was decoupled from sulphur emissions, and its sulphur dioxide production per unit of output increased to 0.021 tons. Sulphur dioxide emissions per unit of economic growth along the southeast coast were 379. 048 tons, which was well below the overall average of 444.573 tons. High-income towns along the southeast coast have achieved clean production breakthroughs, realising a 15% reduction in industrial sulphur dioxide emissions by 2021. Although there were signs of a shift toward clean manufacturing in high administrative ranking cities, most regions of China are transitioning to environmentally friendly manufacturing and suffer from the hardships of green production transformation.

The digital economy, emerging as a new energy source, is driving the transition to green and sustainable urbanization through its significant enabling effects. While the digital economy enhances productivity, replaces traditional energy, and has the potential to transform production models, further investigation is needed to understand its impact on reducing industrial hazards. This article addresses this gap by acknowledging the transformative impact of digital technologies on economic activities, based on the latest expanded EKC theory. The study highlights the energy efficiency of digitalization as a crucial mechanism for reducing industrial hazards. It also highlights a wealth effect associated with the digital economy that may increase pollution, thereby expanding on the Environmental Kuznets Curve Theory. To clarify the interplay of these effects, the study employs a multidimensional estimation method to analyze the dynamic impact of the digital economy on reducing industrial hazards. Empirical estimates indicate a weak causal relationship between the digital economy and sulfur dioxide emissions, with an increase in broadband subscribers having a slight negative effect on emissions reduction. In contrast, the expanding scientific industry significantly reduces emissions, with reductions ranging from −0.535 to −1.423 tons per billion RMB of output (or 1400 million US dollars). Increased gas supply and Foreign Direct Investment (FDI), often tied to energy-intensive industries, positively influence emissions, with FDI showing a significant coefficient of 0.004. The digital economy's impact on industrial wastewater discharge is unclear, but the scientific industry demonstrates a significant negative effect, with coefficients from −2.501 to −2.819 tons per unit output. The most significant finding of this empirical research is that the wealth effects from the digital economy, particularly through increased private sector employment, lead to a substantial rise in wastewater discharge. While digital technology can enhance energy efficiency and optimize production processes, it also encounters challenges such as job displacement and resistance to change. The research promotes sustainable industrial practices, incentivizes digital adoption, and mitigates negative impacts, guiding companies and investors toward improved environmental performance and responsible digital consumption while fostering public awareness of sustainability challenges. The study recommends a comprehensive strategy to address the environmental challenges posed by the digital economy. Policies include implementing population control measures to reduce overall human impact on the environment, promoting eco-friendly education to foster sustainable practices, shifting focus toward quality living overconsumption, and developing sustainable residential planning.