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This article assesses the effect of climate change on livestock production in Sub‐Saharan Africa, for a sample of 45 countries over the period 2000–2021. Using a two‐factor fixed effects panel data model, our results obtained by the two‐way fixed effects estimator show that (i) climate change negatively influences livestock production through high temperatures, while abundant rainfall is beneficial. (ii) Through transmission channels, we find that maize price volatility exacerbates the negative effect of rising temperatures on livestock production, while it reduces the beneficial effect of abundant rainfall. Furthermore, we find that water availability mitigates the adverse effect of rising temperatures on livestock, while enhancing the beneficial effect of rainfall on livestock. Finally, we concede that conflicts reduce the beneficial effect of rainfall on livestock production. To increase livestock production in Sub‐Saharan Africa, we recommend: the practice of pastoralism, based on the production of plants and fodder adapted to climate change, the improvement of animal nutrition, and the inclusion of breeders in the decision‐making process in the cattle industry.

While digital finance and renewable energy consumption (REC) are two timely issues, it remains unclear whether the former affects the latter, especially in developing economies. This paper examines the impact of digital finance on China’s REC between 2011 and 2018 and explores the underlying mechanisms. Results show that digital finance, along with its coverage breadth and usage depth, significantly improved REC in China and that digital finance in the area of credit has had the most significant impact. Additionally, the results show that loan scale and income level are the main mediation variables, through which digital finance affects REC. The findings also suggest that economic growth and technological progress have increased REC in China, while carbon dioxide emissions have had no meaningful effect on this consumption. The results further indicate that policymakers must pay close attention to the role of digital finance when formulating policies on REC. To promote REC and environmental sustainability, developing economies like China should strengthen the breadth and depth of digital finance development, focus on the influence channels of digital finance, and promote economic growth and technological progress.

The global industrial chain and energy supply chain are being reconfigured at an accelerated pace, and the uncertainty of China’s energy supply security is growing significantly. Empowering energy supply chains through the digital economy (diec) has a positive effect on accelerating the transformation of China’s energy supply structure. This paper discusses the effect and mechanisms of the digital economy on energy supply chain efficiency (esce). Specifically, based on the panel data of 112 energy enterprises in China from 2011 to 2019, energy supply chain efficiency and digital economy at the enterprise level were evaluated through three-stage DEA and content analysis, respectively. A two-way fixed effects model and mediation effect mode were adopted to investigate the nexus of diec and esce. The results show that the digital economy improves energy supply chain efficiency, and the conclusion holds water even after a series of robustness tests and endogenous treatment. Meanwhile, its promotion effect is more significant among large enterprises, non-state enterprises and enterprises in high market-oriented regions. The main impact mechanisms are regional industrial agglomeration and technological innovation of enterprises. Based on the above conclusions, it is suggested to take advantage of the industrial aggregation effect and technological innovation effect of the digital economy to further improve the efficiency of the energy supply chain for the purpose of maintaining energy supply security.

Green and sustainable development is unstoppable. The digital economy has driven great changes in production methods and has become a key strength in reshaping global economic structure and achieving sustainable development. Cities are both the mainstay of economic growth and the main source of various environmental pollution problems. Therefore, studying the relationship between urban digital economy and urban green total factor productivity is of great significance. Based on panel data from 252 cities in China 2011–2019, a two-way fixed effects model was used to examine the impact of urban digital economy on urban green total factor productivity. The empirical results indicate that: (1) Urban digital economy has a significant positive impact on urban green total factor productivity. (2) Urban technological-innovation-level and human-capital-structure of play a mediating role in the impact. (3) This impact has regional heterogeneity and resource-based type heterogeneity. The research conclusions are not only valuable supplements to previous research, but also providing reliable instructions for implementing a flexible digital economy policy.

To study the impact mechanism and effect of agricultural digitization on the agricultural field plays a vital role in achieving the target of high-quality agricultural development. There are three perspectives that can be taken to construct the framework of analysis as to the impact mechanism of agricultural digitization on the high-quality development of agriculture: enhancing agricultural production efficiency, optimizing resource allocation and upgrading the industrial structure. Besides, the threshold effect of the education level of the labor force is also analyzed. Based on China’s provincial panel data from 2011 to 2020, the two-way fixed effects model and threshold effect test model are applied to verify the research hypothesis. It has been discovered that agricultural digitization is conducive to promoting the high-quality development of agriculture. Heterogeneity analysis shows that agricultural digitization plays a more significant role in the eastern region than in the central and western regions. There is a single threshold effect that depends on the education level of the rural labor force in the promotion of agricultural digitization to high-quality agricultural development. When the threshold is exceeded, agricultural digitization plays a more significant role in promoting high-quality agricultural development. There are three policy suggestions made to conclude the study. The first one is to improve the construction of agricultural digitization infrastructure. The second one is to pay attention to the differences in the development degree and demand between regions in the process of agricultural digitization construction. The last one is to improve the quality of the rural labor force and the input of scientific and technological talents in the agricultural industry.

With the rapid advancement of artificial intelligence (AI) technology, the global employment structure is undergoing profound transformations, significantly impacting social sustainability. This study utilizes panel data from 30 Chinese provinces spanning the years 2010 to 2022 and applies a two-way fixed-effects model to analyze the impact of AI development on the employment skills structure. The findings indicate that advancements in AI technology significantly suppress the demand for low-skilled labor while markedly enhancing the demand for both middle- and high-skilled labor. The threshold effect analysis reveals a nonlinear relationship between AI advancements and the demand for low-skilled workers. Mediation effect tests demonstrate that technological innovation serves as a mediating factor in AI’s impact on low- and middle-skilled labor but has no significant effect on high-skilled labor. The heterogeneity analysis further indicates that AI’s negative impact on low-skilled female employment is more severe than for males, while its positive impact on high-skilled male workers is significant. Additionally, the employment effects of AI are mainly observed in labor-intensive provinces, with minimal influence in capital-intensive areas. This study suggests harnessing AI’s potential to promote employment while proactively mitigating its disruptive effects on the labor market through enhanced research and development support, strengthened employment security, and coordinated regional economic development, thereby advancing sustainable economic and social progress.

The pollutant emissions of diesel-powered heavy-duty trucks (HDTs) seriously damage the air quality. The promotion of hydrogen fuel cell HDTs through purchase subsidy policy to reduce emissions has become an important approach to control air pollution. This study focuses on the impact of hydrogen fuel cell HDT purchase subsidies on air quality in the context of China, covering the panel data of 31 Chinese cities from 2014 to 2021 and applying a two-way fixed effects model to analyze the contribution of purchase subsidies and hydrogen refueling station construction subsidies to air quality. Results show that (1) the increase in purchase subsidies could improve the air quality by around 6.1% and there is a lag effect. (2) Purchase subsidies make a larger contribution to air quality compared with construction subsidies. (3) Purchase subsidies can improve air quality by reducing carbon emissions in transport industry. In sight of these results, policy makers should emphasize the implementation of purchase subsidies and hydrogen refueling station construction subsidies and stimulate manufacturers to improve the performance of hydrogen fuel cell so as to contribute more to the environment.

Employment is the foundation of social stability and a key factor for economic stability and sustainable development. With the rapid advancement of technology, the impact of technological progress on employment has become a focal point of academic attention. As an emerging industry, the healthcare sector has experienced rapid growth in recent years, driven by the widespread application of scientific and technological innovations. However, at the same time, these advancements have also exerted a significant influence on employment within the healthcare sector. To address this issue, this paper utilizes panel data from publicly listed healthcare firms in the United States between 2013 and 2023. It innovatively measures technological progress through Total Factor Productivity (TFP) and employs a two-way fixed effects model to empirically analyze the impact of technological progress on employment in the healthcare sector from a microeconomic perspective. The findings indicate that a 1% increase in technological progress in the healthcare sector leads to an average 0.116% rise in employment levels. This conclusion remains robust after conducting rigorous robustness checks and addressing endogeneity concerns, with the output effect playing a significant role in this process. Heterogeneity analysis indicates that technological progress significantly promotes employment across various sub-sectors, though the magnitude of this effect varies only slightly among industries. Furthermore, the employment-promoting effect of technological progress is more pronounced in larger firms and those with a higher proportion of fixed assets. Therefore, policies should actively support the improvement of technology levels and management efficiency within the healthcare sector, fully leveraging the potential of technological progress to promote employment, and achieve sustainable development for both the healthcare sector and societal employment.

For a panel data linear regression model with both individual and time effects, empirical studies select the two-way random-effects (TWRE) estimator if the Hausman test based on the contrast between the two-way fixed-effects (TWFE) estimator and the TWRE estimator is not rejected. Alternatively, they select the TWFE estimator in cases where this Hausman test rejects the null hypothesis. Not all the regressors may be correlated with these individual and time effects. The one-way Hausman-Taylor model has been generalized to the two-way error component model and allow some but not all regressors to be correlated with these individual and time effects. This paper proposes a pretest estimator for this two-way error component panel data regression model based on two Hausman tests. The first Hausman test is based upon the contrast between the TWFE and the TWRE estimators. The second Hausman test is based on the contrast between the two-way Hausman and Taylor (TWHT) estimator and the TWFE estimator. The Monte Carlo results show that this pretest estimator is always second best in MSE performance compared to the efficient estimator, whether the model is random-effects, fixed-effects or Hausman and Taylor. This paper generalizes the one-way pretest estimator to the two-way error component model.

Green innovation is an important way to integrate China’s innovation-driven strategy with sustainable development strategy. Adopting the attention-based view in policy implementation analysis, this paper constructs an analytical framework of how the local government’s attention paid to green innovation (LGA-GI) affects green innovation efficiency (GIE). Using the panel data of 30 provincial administrative regions in China from 2009 to 2020, we describe the temporal and spatial characteristics of LGA-GI, empirically test the impact of LGA-GI on GIE through two-way fixed effects models, and then compare the effects in the three stages of green innovation. The major findings are as follows: (1) the LGA-GI in China from 2009 to 2020 shows an upward trend with mild fluctuations, and peaks three times in 2012, 2016, and 2018. The spatial distribution of LGA-GI has changed from a pattern of “low in the middle” (low LGA-GI in the central region) to “continuous highs with scattered lows”. (2) LGA-GI has a significant positive effect on the overall GIE, but the effect is concentrated in the stage of knowledge absorption and commercialization, rather than in the stage of knowledge innovation. The implication of these results is that local governments need to allocate more attention to green innovation and maintain its continuity, and governments at all levels should distribute policy implementation resources based on the characteristics of different green innovation stages.