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The development of green finance and social low-carbon transformation is an essential concern for academia and industry. Based on Chinese provincial panel data spanning the period 2005-2019, we introduce the Cobb-Douglas production function and spatial Durbin and dynamic panel threshold models to deeply analyse the impact of green finance on the low-carbon economy. The mechanism test demonstrates that the scale, technique, and structural effects of green finance play a significant role in the low-carbon economy: they correct capital mismatch, promote green technology innovation, and optimise industrial structure. Meanwhile, green finance not only promotes the local low-carbon economy construction process, but also generates spatial spillover effects on neighbouring regions; however, there is regional heterogeneity in the impact of the transmission mechanism. Furthermore, only when capital mismatch is severe, and the low-end industrial structure poor is the positive impact of green finance on the low-carbon economy highlighted based on scale and structural effects; the ability of green finance to contribute to the low-carbon economy through the technique effect has been more stable and significant. This emphasises that green technology innovation is key to supporting low-carbon development in the long run.

Internet finance, a byproduct of the close coupling of Internet technology with the financial sector, has contributed significantly to the growth of local economies. This essay first examines the relationship between regional economic growth and online financing. Second, the constraint mechanism between Internet finance and local economic growth is examined using the efficacy function of the coupling degree evaluation model. The consistency of the spillover impact of the Internet finance cluster on the economic growth of the region and the surrounding areas is examined using the spatial effect decomposition. In 2021, the Moran indices of W1, W2, and W3 were 0.418, 0.123, and 0.341, respectively. The correlation between Internet financing and local economic growth is strengthened, while the influence factor of economic and geographical distance is reduced.

To address the challenges posed by the living environment and promote sustainable development, the Chinese government implemented a new environmental protection law in 2015. Based on the provincial panel data of 30 provinces, autonomous regions, and municipalities in China from 2010 to 2019, the spatial Durbin model is used to investigate the impact of environmental regulation on the employment effect of high-tech industries, and the spatial effect decomposition is used to further clarify the specific impact of environmental regulation on the employment of high-tech industries. The research finds that: Firstly, at the present stage, environmental regulation in China remains at a relatively low level. The employment generation effect of environmental regulation on high-tech industries is insufficient to offset the employment loss effect. Strengthening environmental regulation in the short term is unfavorable for employment in high-tech industries. Secondly, adjacent regions adopt a strategy of competitive differential environmental regulation between governments. The local government relaxes environmental regulation to increase employment, while the neighboring government strengthens environmental regulation to promote industrial upgrading. This approach benefits local employment in high-tech industries in the short term but hinders the sustainable development of high-tech industries. Thirdly, environmental regulation exhibits significant negative spatial spillover effects. Strengthening local environmental regulation will suppress the growth of high-tech industry employment in neighboring areas, and the spatial spillover effect of environmental regulation is primarily influenced by geographic location.

Using panel data from 30 provinces in China from 2005 to 2020, this paper uses a spatial double difference model to evaluate the policy impact of the “grain-to-feed” policy on feed grain production in pilot areas and adjacent spatial areas. Research has found that the “grain-to-feed” policy has a significant impact on the feed grain production in pilot areas and can significantly increase the feed grain production in pilot areas by about 2.71 million tons. The “grain-to-feed” policy has strengthened the positive connection between pilot areas and adjacent pilot areas, increased feed grain production, and has a significant spatial spillover effect. Robustness analysis shows that whether using different methods to measure spatial adjacency or using different standards to distribute subsidies, the “grain-to-feed” policy can significantly increase feed grain production, narrow the supply and demand gap of feed grain, and ensure feed grain security. Further analysis shows that the “grain-to-feed” policy can not only ensure the security of feed grain for the current and next periods but also promote the increase in farmers’ income, which is long-term and sustainable. Compared with non-pilot areas, the “grain-to-feed” policy can mitigate the negative impact of wage–price signals on feed grain production in pilot areas. It is recommended that government departments accelerate the transformation of food security concepts, establish a “Big Food Perspective”, gradually promote the pilot of the “grain-to-feed” policy nationwide, increase the subsidy amount of the “grain-to-feed” policy, increase financial support for scientific and technological research and achievement transformation in the field of feed grain, prevent the impact of economic price signal fluctuations on feed grain production, and effectively ensure the security of feed grain in China.

Air pollution control is an urgent problem in the field of environment, and it is crucial to accurately identify emission driving factors and collaborative emission reduction paths. In order to construct and analyze the driving mechanism of atmospheric pollutant emissions and explore the potential for regional collaborative emission reduction, an innovative three-stage progressive analysis framework was developed by combining Logarithmic Mean Divisia Index (LMDI) decomposition and Geographically Weighted Regression (GWR), which includes factor decomposition, spatial modeling, and collaborative optimization. Through empirical analysis, it was found that the energy intensity effect in Tangshan city reduces emissions by an average of −14.834 million tons per year, becoming the core driving force. The synergistic emission reduction ratio of SO2-PM2.5 in the Beijing Tianjin Hebei region reached 1: 0.38, with an average annual emission reduction of 297000 tons and a regional synergy index of 0.85 ( p  < 0.01), significantly better than other pollutant combinations. The adjusted R2 of the GWR model reached 0.86, the residual Moran’s I index was 0.07, and the proportion of significant variables reached 75%, which is 15.28% higher than other models. In addition, the Akaike information criterion corrected by the GWR model was reduced by an average of 12.78% compared to other models. The results indicated that the synergistic effect of multi factor decomposition and spatial heterogeneity analysis could significantly enhance the regional adaptability of emission reduction strategies, providing scientific support for cross regional collaborative governance.

Soils store more carbon than other terrestrial ecosystems1,2. How soil organic carbon (SOC) forms and persists remains uncertain1,3, which makes it challenging to understand how it will respond to climatic change3,4. It has been suggested that soil microorganisms play an important role in SOC formation, preservation and loss5–7. Although microorganisms affect the accumulation and loss of soil organic matter through many pathways4,6,8–11, microbial carbon use efficiency (CUE) is an integrative metric that can capture the balance of these processes12,13. Although CUE has the potential to act as a predictor of variation in SOC storage, the role of CUE in SOC persistence remains unresolved7,14,15. Here we examine the relationship between CUE and the preservation of SOC, and interactions with climate, vegetation and edaphic properties, using a combination of global-scale datasets, a microbial-process explicit model, data assimilation, deep learning and meta-analysis. We find that CUE is at least four times as important as other evaluated factors, such as carbon input, decomposition or vertical transport, in determining SOC storage and its spatial variation across the globe. In addition, CUE shows a positive correlation with SOC content. Our findings point to microbial CUE as a major determinant of global SOC storage. Understanding the microbial processes underlying CUE and their environmental dependence may help the prediction of SOC feedback to a changing climate.

Researchers seeking to establish causal relationships frequently control for variables on the purported causal pathway, checking whether the original treatment effect then disappears. Unfortunately, this common approach may lead to biased estimates. In this article, we show that the bias can be avoided by focusing on a quantity of interest called the controlled direct effect. Under certain conditions, the controlled direct effect enables researchers to rule out competing explanations—an important objective for political scientists. To estimate the controlled direct effect without bias, we describe an easy-to-implement estimation strategy from the biostatistics literature. We extend this approach by deriving a consistent variance estimator and demonstrating how to conduct a sensitivity analysis. Two examples—one on ethnic fractionalization’s effect on civil war and one on the impact of historical plough use on contemporary female political participation—illustrate the framework and methodology.

Understanding and characterizing treatment effect variation in randomized experiments has become essential for going beyond the \"black box\" of the average treatment effect. Nonetheless, traditional statistical approaches often ignore or assume away such variation. In the context of randomized experiments, this article proposes a framework for decomposing overall treatment effect variation into a systematic component explained by observed covariates and a remaining idiosyncratic component. Our framework is fully randomization-based, with estimates of treatment effect variation that are entirely justified by the randomization itself. Our framework can also account for noncompliance, which is an important practical complication. We make several contributions. First, we show that randomization-based estimates of systematic variation are very similar in form to estimates from fully interacted linear regression and two-stage least squares. Second, we use these estimators to develop an omnibus test for systematic treatment effect variation, both with and without noncompliance. Third, we propose an R 2 -like measure of treatment effect variation explained by covariates and, when applicable, noncompliance. Finally, we assess these methods via simulation studies and apply them to the Head Start Impact Study, a large-scale randomized experiment. Supplementary materials for this article are available online.

Because eigenvector spatial filtering (ESF) provides a relatively simple and successful method to account for spatial autocorrelation in regression, increasingly it has been adopted in various fields. Although ESF can be easily implemented with a stepwise procedure, such as traditional stepwise regression, its computational efficiency can be further improved. Two major computational components in ESF are extracting eigenvectors and identifying a subset of these eigenvectors. This paper focuses on how a subset of eigenvectors can be efficiently and effectively identified. A simulation experiment summarized in this paper shows that, with a well-prepared candidate eigenvector set, ESF can effectively account for spatial autocorrelation and achieve computational efficiency. This paper further proposes a nonlinear equation for constructing an ideal candidate eigenvector set based on the results of the simulation experiment.

The seismic effects of complex, deep, and inhomogeneous sites constitute a significant research topic. Utilizing geological borehole data from the Suzhou urban area, a refined 2D finite element model with nonuniform meshes of a stratigraphic section crossing the Suzhou region was established. Within the ABAQUS/explicit framework, the spatial inhomogeneity of soils, including the spatial variation of S-wave velocity structures, was considered in detail. The nonlinear and hysteretic stress–strain relationship of soil was characterized using a non-Masing constitutive model. Ricker wavelets with varying peak times, peak frequencies ( f p ), and amplitudes were selected as input bedrock motions. The analysis revealed the spatial distribution characteristics of 2D nonlinear seismic effects on the surface of deep and complex sedimentary layers. The surface peak ground acceleration (PGA) amplification coefficients initially increased and then decreased as f p increases. The surface PGA amplification was most pronounced when the f p is close to the site fundamental frequency. Additionally, when f p  = 0.1 Hz, the surface PGA amplification was found to depend solely on the level of bedrock seismic shaking, with amplification factors ranging from 1.20 to 1.40. Furthermore, the ensemble empirical mode decomposition components of seismic site responses can intuitively reveal the variations in time–frequency and time–energy characteristics of Ricker wavelets as they propagate upward from bedrock to surface.