The development of digital villages plays a critical role in advancing green agriculture steadily and with high quality. This study measures the efficiency of agricultural water resource allocation using provincial-level rural data from 2011 to 2022 and a super Slacks-Based Measure model accounting for non-desirable outputs. Using the ArcGIS tool, this study illustrates the spatiotemporal patterns and provincial clustering of the green allocation of agricultural water resources. A spatial Durbin model further examines interactions between digital villages and water resource efficiency. Findings indicate the following: (1) The green water resource efficiency in China’s 30 provinces showed a gradual, steady increase, albeit with notable regional differences, particularly a “northwestern depression” in spatial distribution. (2) Moran’s I index indicates a strong positive spatial correlation between digital villages and green water resource efficiency, manifested as either “high–high” or “low–low” clustering. (3) The spatial Durbin model results demonstrate that digital villages enhance the green water resource efficiency of neighboring areas, a phenomenon referred to as the “Matthew effect”. (4) Different aspects of digital village construction, operation, and lifestyle positively influence green water resource efficiency, while digital loops have a negative impact. Aligning resource allocation efficiency with the establishment of digital village infrastructure is paramount. This symbiotic relationship bolsters the structural foundation of agricultural production, optimizing resource utilization and fostering technological advancements in rural settings.

While the core objective of the Paris Agreement is to limit the increase in global average temperature (GAT) to 2 °C in the 21st century and to work towards limiting it to 1.5 °C, globalization and the configuration of production processes around global value chains (GVCs) have emerged as key factors explaining the recent evolution of environmental and economic indicators. In this context, this paper takes trade-implied carbon emissions as the entry point of the problem, uses MRIO to calculate the production-side and consumption-side carbon emissions, measures the forward and backward production lengths of GVCs according to the WWYZ method, and then constructs an econometric regression model to empirically analyze the environmental effects of GVC embeddedness. The results of the study show that, firstly, the forward and backward production length of GVCs is positively correlated with the production-side and consumption-side carbon emissions. Forward production length has a greater impact on carbon emissions on the production side, and backward production length has a greater impact on carbon emissions on the consumption side. Secondly, compared with developed countries, the length of forward and backward production has a more pronounced positive impact on carbon emissions in developing countries. Thirdly, as the global production chain continues to extend, the scale effect, structural effect, technological effect, and environmental regulation effect will all contribute to carbon emissions. Accordingly, countries or regions should continuously optimize production layout and processes to reduce the length of the production chain, realize lean manufacturing through automation and intelligence, and then move up the global value chain to play a role in carbon emission reduction through structural upgrading, technological progress, and environmental regulation.

In the macro context of promoting sustainable development and achieving net zero emissions, the role of green technology innovation, renewable energy utilization and environmental policy is crucial. However, there is still a lack of consistent empirical evidence regarding the combined emission reduction effect of these three factors in OECD countries. This study aims to empirically examine the combined impact of green technology innovation (GTI), renewable energy consumption (REC), and environmental taxes (ETAX) on carbon dioxide emissions. We expect that the former two will effectively reduce emissions, while the effect of environmental taxes depends on their design. Based on the panel data of 35 OECD economies from 1990 to 2019, this study adopts the augmented mean group (AMG) as the main estimation method, and uses the common correlation mean group (CCEMG) for the robustness test. To control potential endogenous issues, the difference generalized method of moments (GMM) is also employed for estimation. The causal relationship between variables is tested using the Dumitrescu–Herlin method. The results show that, as expected, GTI and REC have a significant negative impact on carbon dioxide reduction. However, ETAX is positively correlated with carbon emissions and does not have statistical significance, which deviates from the ideal policy effect and suggests that there may be efficiency bottlenecks in the current tax design. The causality test further reveals that there is a significant two-way causal relationship between CO₂ emissions and GTI, REC, ETAX, GDP, and fossil fuel consumption (FEC). Therefore, it is recommended that OECD countries give priority to expanding investment in green technologies and renewable energy infrastructure and re-evaluate and optimize environmental tax policies to effectively promote the transition to a low-carbon economy.

In the macro context of promoting sustainable development and achieving net zero emissions, the role of green technology innovation, renewable energy utilization and environmental policy is crucial. Based on the panel data of 35 OECD economies from 1990 to 2019, this study adopts the augmented mean group (AMG) as the main estimation method, and uses the common correlation mean group (CCEMG) for robustness test, and tests the causal relationship between variables through Dumitrescu-Hurlin method. The results show that both GTI and renewable energy consumption have a significant positive impact on CO 2 emission reduction. Environmental taxes are positively correlated with carbon emissions, but not statistically significant, and the CCEMG estimation results are consistent with AMG. The causality test further reveals that there is a significant bidirectional causality among the variables. Therefore, it is recommended that OECD countries give priority to expanding investment in green technologies and renewable energy infrastructure, and re-evaluate and optimize environmental tax policies to effectively promote the transition to a low carbon economy.