Digital transformation and synergistic reduction in pollution and carbon Emissions--An analysis from a dynamic capability perspective.

Pollution control and carbon emission reduction pose significant challenges for developing countries, and achieving synergistic reductions in pollution and carbon emissions (SRPCs) has emerged as the optimal choice. Digitization, a prevailing trend in the current era, presents new opportunities for realizing the SRPC. We utilize data from Chinese A-share listed companies from 2007 to 2022 to investigate the impact of corporate digital transformation on the SRPC. The research reveals the following. (1) Enterprise digital transformation significantly promotes the SRPC, a conclusion validated through a series of robustness tests. However, the SRPC resulting from digital transformation is characterized by weak synergy, which is primarily observed in the coordinated reduction in air pollution and carbon emissions. (2) This effect is notably stronger for companies near environmental regulatory authorities, those receiving government environmental subsidies, and state-owned enterprises (SOEs). (3) Enhancing managerial collaborative management capabilities, promoting corporate technological innovation capabilities, and alleviating financing pressure are the primary mechanisms at play. This research provides important policy and practical insights for promoting the SRPC in the context of digitization, fostering sustainable development.

Prediction of the roughness coefficient for drainage pipelines with sediments using GA-BPNN.

Accurate prediction of the roughness coefficient of sediment-containing drainage pipes can help engineers optimize urban drainage systems. In this paper, the variation of the roughness coefficient of circular drainage pipes containing different thicknesses of sediments under different flows and slopes was studied by experimental measurements. Back Propagation Neural Network (BPNN) and Genetic Algorithm-Back Propagation Neural Network (GA-BPNN) were used to predict the roughness coefficient. To explore the potential of artificial neural networks to predict the roughness coefficient, a formula based on drag segmentation was established to calculate the roughness coefficient. The results show that the variation trend of the roughness coefficient with flow, hydraulic radius, and Reynolds number is consistent. With the increase of the three parameters, the roughness coefficient decreases overall. Compared to the traditional empirical formula, the BPNN model and the GA-BPNN model increased the determination factors in the testing stage by 3.47 and 3.99%, respectively, and reduced the mean absolute errors by 41.18 and 47.06%, respectively. The study provides an intelligent method for accurate prediction of sediment-containing drainage pipes roughness coefficient.

Environmental decentralization, environmental public service, and public health: evidence from 289 cities in China.

Effective supply of environmental public services (EPS) is important to guarantee the mitigation of residential pollution exposure risk. This study analyzes the impact of the supply efficiency of EPS on residential health through the sample of 289 cities in China by fixed-effect model and threshold regression model. The results show that: (1) Improving the efficiency of EPS can significantly increase the efficiency of pollution control, and improve the environmental quality and health level of residents. (2) Improving the efficiency of EPS can have a greater positive effect on residents' health in areas with high-level population aging, poor medical condition, and low population density. (3) Environmental decentralization weakens the positive effect of EPS on residential health. Giving more power of environmental management to local governments may be detrimental to pollution control. Therefore, differentiated strategies of EPS should be developed to realize the equalization of basic public services with healthcare and environmental protection. It is necessary to strengthen central environmental supervision and establish resident offices for environmental supervision.

Analysis of groundwater variation in the Jinci Spring area, Shanxi Province (China), under the influence of human activity.

Jinci Spring is one of the most famous karst spring in Northern China and is the main drinking and irrigation water source of Taiyuan city, Shanxi Province. It has special significance in terms of humanities and water resources. Because of the continuous over-exploitation of groundwater, the water level of groundwater has been decreased significantly. The flow of Jinci Spring has decreased year by year since the 1970s, and in 1994 it cut off completely. The flow cutoff has brought huge losses to the local economy and negatively impacted the ecological environment. From the hydrogeological conditions of Jinci Spring area to study, the dynamic change in karst groundwater level will provide a basis for the protection and rational exploitation of karst water in the spring area. The previous research on Jinci Spring area was limited to the spring flow and water level changes, lack of analysis to the overall spring area and the decay process of spring flow. This paper takes the overall spring area as research object. According to groundwater level data over 1961-2012, the period is divided into two phases of decline and recovery. Human activities in the region and corresponding groundwater evolution at various stages were systematically analyzed, and the reservoir capacity was calculated for each period of decline. The results show that the average reservoir capacity decreased from 920 (1954-1960) to 116 (1961-1977) to 31 (1978-1994) million m3. The important effect of "Water Resumption" project on groundwater in the area during the remediation period was also analyzed. In the light of observed increases in groundwater level during this stage, the project has a notably positive effect.

Clarifying urban flood response characteristics and improving interpretable flood prediction with sparse data considering the coupling effect of rainfall and drainage pipeline siltation.

With climate warming and accelerated urbanisation, severe urban flooding has become a common problem worldwide. Frequent extreme rainfall events and the siltation of drainage pipes further increase the burden on urban drainage networks. However, existing studies have not fully considered the effects of rainfall and pipeline siltation on the response characteristics of flooding when constructing numerical models of urban flooding simulations. To solve this problem, a surface-subsurface coupling model was constructed by combining the Saint-Venant equation, Manning equation, a one-dimensional pipeline model (SWMM), and a two-dimensional surface overflow model (LISFLOOD-FP). Then, the SWMM model considering pipeline siltation and the two-dimensional surface overflow model (LISFLOOD-FP) were coupled with the flow exchange governing equation, and the urban flooding response characteristics considering the coupling effect of "rainfall and drainage pipeline siltation" were analysed. To enhance the solvability of waterlogging prediction, an intelligent prediction model of urban flooding based on Bayes-CNN-BLSTM was established by combining a convolutional neural network (CNN), bidirectional long short-term memory neural network (BLSTM), Bayesian optimisation (Bayes), and an interpretable loss function error correction method. The actual rainfall events and flooding processes recorded by the monitoring equipment at Huizhou University were used to calibrate and verify the model. The results show that in the Rainfall 1 and Rainfall 2 scenarios, the overload rates of the pipelines in the current siltation scenario were 60.06 % and 68.37 %, respectively, and the proportions of overflow nodes were 24.87 % and 25.89 %, respectively. When the drainage network was initially put into operation, the overload rates of the pipeline were 36.67 % and 41.16 %, and the overflow nodes accounted for 3.05 % and 4.06 %, respectively. The inundated area and volume of urban flooding increased when the combined siltation coefficient (CSC) was 0.2; therefore, two desilting schemes were determined. Under Rainfall 1, Rainfall 2, and the four rainfall recurrence periods, the Bayes-CNN-BLSTM model had clear advantages in terms of accuracy, reliability, and robustness.

Digital financial development, synergistic reduction of pollution, and carbon emissions: evidence from biased technical change.

Environmental pollution control and greenhouse gas emissions reduction have become the main ecological protection issues. The digital transformation of the financial sector provides a vital opportunity to holistically promote environmental governance. This article incorporates the synergistic reduction of pollution and carbon emissions into the environmental governance system of digital financial development. Using panel data from 280 cities in China between 2011 and 2018, we examine the impact of digital financial development on the synergistic reduction of pollution and carbon emissions. We find that (1) digital finance development can significantly improve the synergistic reduction of pollution and carbon emissions, effectively lowering carbon emissions while reducing pollution. External environmental concerns can further unleash the potential for digital finance development to enhance its synergistic reduction of pollution and carbon emissions. (2) The key transmission mechanism lies in the dual guidance of digital finance development toward biased technical change, i.e., toward energy-saving elements on the input side and toward reduced pollution output on the output side, thereby inducing the synergistic reduction of pollution and carbon emissions. (3) The synergistic effect of digital finance development on pollution and carbon reduction depends on the necessary regional development endowment, such as strong green technology innovation capabilities, lower traditional financial accessibility, and carbon sink reserves. This study expands the understanding of the environmental effects of digital finance development and offers crucial insights for exploring the optimal development path under green strategies.

Spatiotemporal evolution of water ecological footprint based on the emergy-spatial autocorrelation method.

To quantify and analyze the human demand for water resources and the available supply of water resource systems, this study combined emergy analysis and spatial autocorrelation analysis to establish a quantification and analysis system for water ecological footprint (WEF). First, the emergy theory of ecological economics and WEF were combined to propose an emergy quantification method for WEF and water ecological carrying capacity (WEC). Based on the spatial autocorrelation method, three-dimensional ecological footprint indicators (footprint size and depth) were introduced to analyze the spatial correlation and spatial aggregation of capital flow occupation and capital stock consumption in the water resource system. Using the Yellow River Basin (YRB) as the study area to verify the applicability of the WEF quantification and analysis system based on the emergy-spatial autocorrelation method, the following results were obtained. (1) From 2003 to 2018, the per capita WEF of the YRB generally showed a slow growth trend. (2) Compared to the upper and lower reaches of the YRB, the middle reaches had a higher WEF, and the WEC of the YRB was generally high in the west and low in the east. (3) Utilization of the water resources capital in the basin was generally unsustainable. It is necessary to take measures to promote rational allocation and efficient utilization of water resources for the coordinated development of society, the economy, and the environment in the YRB. (4) The emergy-spatial autocorrelation method is applied to basin/region water sustainability studies for decision makers.

Dynamic variations of terrestrial ecological drought and propagation analysis with meteorological drought across the mainland China.

Ecological drought is a complex comprehensive process in which the water conditions for normal growth and development of vegetation are changed due to insufficient water supply. In this study, based on the remotely sensed vegetation health index (VHI) and the Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) datasets from 1982 to 2020 in China, the Breaks For Additive Seasons and Trend algorithm (BFAST) was used to analyze the dynamic variations of ecological drought, the standardized regression coefficient method was applied to identify the primary drivers of ecological drought, and the regression analysis was adopted to reveal the coupling effect of atmospheric circulation factors on ecological drought. The results indicated that: (1) the ecological drought showed an overall decreasing trend during 1982-2020 in China, with a negative mutation point that occurred in April 1985; (2) spring drought and summer drought were more likely to occur in the South China, and autumn drought and winter drought were more likely to appear in the Sichuan Basin; (3) the propagation time from meteorological to ecological drought was shorter in summer (2.67 months) and longer in winter (7 months), with average r values of 0.76 and 0.53, respectively; (4) the Trans Polar Index (TPI), Arctic Oscillation (AO) and El Niño-Southern Oscillation (ENSO) had important impacts on ecological drought, which can be used as input factors of drought early warning system to improve the accuracy of drought prediction.

Study of spatial distribution characteristics of river eco-environmental values based on emergy-GeoDa method.

River eco-environmental value assessment is indispensable for the optimal allocation of watershed water resources. In this study, river eco-environmental values were divided into the values inside and outside the river based on the energy transfer and transformation of the water cycle. Their spatial distribution characteristics (spatial distribution map, spatial autocorrelation, and spatial aggregation) of 67 regions (states, leagues, and cities) in the Yellow River Basin (YRB) were analysed by combining emergy theory and GeoDa 1.14 software (emergy-GeoDa), and the significance of the results was tested. The results showed that: (1) the eco-environmental values inside the river were higher than those outside in the YRB, proving that eco-environmental water inside the river should be guaranteed and water consumption outside the river should not occupy water inside the river from the perspective of value; (2) the spatial distributions of eco-environmental values inside and outside the river were uneven, but obvious spatial aggregations were observed; (3) high- and low-value aggregations of eco-environmental values were observed inside the river in the lower and upper reaches of the YRB, respectively; (4) high- and low-value aggregations of eco-environmental values were observed outside the river in the middle and upper reaches of the YRB, respectively. It was suggested that ecological conservation and high-quality development should be considered as the goals for consolidating the river eco-environmental values in high-value areas and promoting in low-value areas, and low-value areas should be improved by high-value areas while allocating water resources in the YRB. These results provide suggestions for the sustainable development of river eco-environmental system in the YRB from a spatial perspective. In addition, the analysis method is also applicable for studying the spatial distribution characteristics of the values generated by the water and energy cycles of other regions.

Management implications of spatial-temporal variations of net anthropogenic nitrogen inputs (NANI) in the Yellow River Basin.

It is an important content of environment management to accurately identify the time change and spatial distribution of net anthropogenic nitrogen inputs (NANI) in the river basin. In order to develop a unified management and diverse control strategy that fits the characteristics of the basin, this study establishes the NANI-S model combining the NANI model with the spatial autocorrelation analysis method, which is a quantification-analysis-control process, and takes the 70 prefecture-cities in the Yellow River Basin (YRB) as the study area. The result shows that (1) the NANI of YRB increased first and then decreased with an average NANI value of 6787.59 kg/(km2·a), showing that the overall N pollution situation of the YRB shows a trend of improvement in nitrogen (N) fertilizer input as the main source, and the average contribution rate was 47.45%. (2) There were obvious spatial differences in the NANI in the YRB because the global Moran's I fluctuated between 0.67 and 0.78. Cities with high NANI clustered in the middle and lower reaches, while low NANI clustered in the upper reaches. (3) Improving fertilizer utilization rate and industrial and domestic sewage treatment capacity was the key point of N control. Based on the results, practical policy recommendations for water pollution management were constructed, which provides a scientific basis for pollution prevention and high-quality development in the basin. In addition, this analysis method can also be applied to other basin N management studies.