Hong Yan’s research while affiliated with Zhejiang University and other places

The efficient management of water resources in Chinese agriculture is crucial for ensuring food security and mitigating environmental consequences such as water scarcity and pollution. Agricultural technological innovation is crucial in optimizing agricultural practices and making them more sustainable. To this end, this study investigates the dynamic relationship between agricultural technological innovation and total-factor agricultural water usage efficiency (TFAWUE) in Chinese provinces from 2000 to 2020. The study utilizes the Data Envel-opment Analysis (DEA) Malmquist productivity index approach to measure the overall efficiency of water usage in agriculture, known as total-factor agricultural water usage efficiency (TFAWUE). The findings suggest that the mean TFAWUE score of Chinese provinces is 1.1356, surpassing a value of 1. It illustrates that Chinese provinces witnessed a growth of 13.56 in TFAWUE over the study period. Technological change is the primary determinant of growth in the TFAWUE, as technology change (TC) is higher than efficiency change (EC). Subsequently, by employing a rigorous econometrics series, this study provides valuable insights into the intricate dynamics of agricultural technological innovation and its impact on total-factor agricultural water usage efficiency. The study constructs a composite multidimensional index of agricultural technological development, encompassing various technologies pivotal to the agriculture sector. Analysis shows that agricultural technologies enable farmers to implement water conservation practices effectively to enhance agricultural water usage efficiency. However, the farm scale reduces the efficiency of agricultural water usage. Additionally, sprinkler technology positively enhances water usage efficiency in agriculture. These findings provide valuable insights for policymakers in the agricultural sector, offering guidance on sustainable practices and policies for managing water resources in conjunction with improvements in agricultural technologies.

This study investigates the simultaneous influence of the digital economy, environmental technologies, business activity, and institutional quality on both the environment and economic growth in G7 economies from 1996 to 2020. The study provides an in-depth analysis to investigate the influence of institutional quality, particularly the regulatory environment, on business activity. Employing a rigorous methodology encompassing correlation analysis, long-term examination using Driscoll and regression estimators, and the utilization of various digital economy indicators such as internet usage and cell subscriptions, we uncover significant insights. The findings underscore the substantial impact of digital economies in mitigating carbon emissions and driving economic growth at an accelerated rate. Moreover, the study reveals that certain regulatory constraints on corporate operations can paradoxically facilitate carbon emission management while also fostering economic expansion. The study validates the presence of an inverted U-shaped Environmental Kuznets Curve (EKC) in G7 economies. This suggests that there is a specific point at which economic activities start to contribute more to carbon emissions. Moreover, the study highlights the importance of achieving a balance between economic growth driven by foreign direct investment and the goals of environmental sustainability. Environmental technology is becoming increasingly important in the regulation of emissions. Significantly, the study highlights the need to enhance the quality of implementing institutional regulations. It suggests that G7 economies can improve both environmental quality and economic growth by adopting superior regulatory methods. These findings are relevant for governments seeking economic growth and environmental protection. They suggest the need for specific policy actions to accomplish sustainable development goals.

The efficient and sustainable management of forestry resources is crucial in ensuring economic and societal sustainability. The Chinese government has invested significantly in regulations, afforestation, and technology to enhance the forest resource efficiency, reduce technological disparities, and boost productivity growth. However, the success level of this undertaking is unclear and worth exploring. To this end, this study applied DEA-SBM, meta-frontier analysis, and the Malmquist productivity index to gauge the forest resource efficiency (FRE), regional technology heterogeneity (TGR), and total factor productivity growth (MI) in 31 Chinese provinces for a study period of 2001–2020. Results revealed that the average FRE was 0.5430, with potential growth of 45.70%, to enhance the efficiency level in forestry resource utilization. Anhui, Tibet, Fujian, Shanghai, and Hainan were found to be the top performers in forestry utilization during the study period. The southern forest region was ranked highest, with the highest TGR of 0.915, indicating advanced production technologies. The average MI score was 0.9644, signifying a 3.56% decline in forestry resource productivity. This deterioration is primarily attributed to technological change (TC), which decreased by 5.2%, while efficiency change (EC) witnessed 1.74% growth over the study period. The Southern Chinese forest region, indicating an average 3.06% increase in total factor productivity, ranked highest in all four regions. Guangxi, Tianjin, Shandong, Chongqing, and Jiangxi were the top performers, with prominent growth in MI. Finally, the Kruskal–Wallis test found a significant statistical difference among all four regions for FRE and TGR.

The objective of this study is to examine the impact of climate change on forestry efficiency (FRE) and total factor productivity change (TFPC) in 31 provinces of China for a study period of 2001–2020. Additionally, the study aims to evaluate the success level of governmental initiatives used to mitigate climate change. Using the DEA-SBM, this study estimates the forestry efficiency for 31 Chinese provinces and seven regions. Results indicate that the average forestry efficiency score obtained is 0.7155. After considering climatic factors, the efficiency level is 0.5412. East China demonstrates the highest average efficiency with a value of 0.9247, while the lowest score of 0.2473 is observed in Northwest China. Heilongjiang, Anhui, Yunnan, and Tibet exhibit the highest efficiency scores. Mongolia, Heilongjiang, Sichuan, Hebei, and Hunan are the five provinces most affected by climate change. This study’s findings indicate that the average total factor forestry productivity (TFPC) is 1.0480, representing an increase of 4.80%. The primary determinant for change is technology change (TC), which surpasses efficiency change (EC). Including climate variables reduces total factor productivity change (TFPC) to 1.0205, mainly driven by a decrease in TC. The region of South China exhibits the highest total factor productivity change (TFPC) with a value of 1.087, whereas both Northeast China and Central China observe falls below 1 in TFPC. The Mann–Whitney U test provides evidence of statistically significant disparities in forestry efficiency and TFPC scores when estimated with and without incorporating climate factors. Kruskal–Wallis found a statistically significant difference in FRE and TFPC among seven regions.

This study explores the multi-dimensional relationships between technology, fiscal decentralization, and forest resource efficiency, and the pivotal role played by the digital economy as a mediator in 2002–2020. First, this study evaluates the Chinese provinces’ forest resource efficiency using multi-dimensional inputs and outputs of forest sectors. Further, we use two sorts of technology: high-technology expenditure and forest technology education. Fiscal decentralization in terms of local government expenditure on forest resources makes the study innovative and richer in analysis. A SBM-DEA analysis showed that the Anhui, Beijing, Jiangsu, Shanghai, and Zhejiang provinces have the highest efficiency scores, implying very efficient forest resource management. Subsequently, the robust econometric estimator Driscoll and Kraay is applied. The study’s findings disclose that both dimensions of technology increase the Chinese provinces’ forest resource efficiency through technological expenditure and forest technology education. Fiscal decentralization towards forest resource management expenditure increases the efficiency of forests. Urbanization and economic development reduce the efficiency of forests. The digital economy can effectively help to improve the efficiency of forest resources. The presence of moderating effects reveals that the influence of the digital economy on forest resource efficiency is positive when it is coupled with economic development, fiscal decentralization, technology, and urbanization.