Journal of Environmental Management最新文献

Soundscape appropriateness (SA) provides supplemental information on the matching degree between auditory information and the surrounding scene in soundscape perception. This indicator has been integrated into the standard ISO process for collecting soundscape data, forming a component of the sound quality assessment questionnaire. However, its role in soundscape quality assessment has not been fully understood. Herein, we present the findings from soundscape data collected from Beiling Park in Shenyang, China. A method was developed that integrates mediation effect models with multiscale geographically weighted regression models to explore the mediating role of SA in the impact of sound source types on soundscape quality, as well as the spatial heterogeneity of this mediation effect. The results confirm that SA does mediates the influence of sound source types on acoustics comfort (AC). Specifically, natural sounds (indirect effect/total effect = .19/.19), traffic sounds (indirect effect/total effect = -.46/-.65), and commercial sounds (indirect effect/total effect = -.25/-.12) impact the perception of AC by either enhancing or reducing SA. Moreover, the relationships among variables depicted in this model demonstrate spatial heterogeneity, demonstrating that in urban open spaces with complex constructures, local spatial models may be needed for soundscape assessment. The research reaffirms the significance of SA in urban open spaces. In terms of practical implications for urban and landscape planners, when sound sources cannot be controlled or altered, coordinating between the sound and the surrounding environment through landscape optimisation could also improve the quality of the soundscape through enhancing SA and help achieve the goal of creating healthy urban open spaces.

This study examines how stock market returns in emerging BRICS economies respond to growing physical and transition climate risks. To capture the physical climate risk, we use the frequency of natural disasters, the number of people affected by natural disasters, temperature anomaly, and precipitation anomaly. For transition risk, we included two climate-policy uncertainty measures. First, we conduct a panel-level analysis using a cross-sectionally augmented autoregressive distributed lag model. Second, for country-level analysis, we applied the augmented autoregressive distributed lag model to the monthly dataset from January-2000 to March-2023. The empirical results show that an increase in transition climate risk causes a significant and negative shock to stock returns, both in the short- and long-term in the panel and across each BRICS country. Second, we find that physical climate risk indicators have a significant and negative impact on stock returns in China, India, and South Africa, but not in Brazil or Russia. We conclude that the impact of physical climate risk on stock returns is country-specific, and that the impact of transition climate risk is widespread. These findings provide important insights for investors, regulators, hedgers, portfolio managers, and policymakers regarding policy formulation and future investment strategies.

Considering how crucial environmental quality is to development, production often takes precedence over the development process when certain macroeconomic policies are being implemented. This phenomenon has been the subject of several studies conducted in various regions and nations. In this context, the recent article explores the nonlinear effects of industrial output, renewable energy, technological innovations, energy efficiency, and urbanization on CO₂ emissions in the top ten industrialized countries. It recommends contradictory policy approaches due to its reported conflicting outcomes, opening up new research directions. To this end, the study relies on advanced econometric tools such as panel QARDL (Quantile Autoregressive Distributed Lag) and the nonparametric quantile Granger causality (NPQGC) test to attain robust results. The findings suggest that industrial output and urbanization significantly deteriorate environmental quality by increasing CO₂ emissions across various time horizons. However, renewable energy, technological innovations, and energy efficiency have a significant influence towards enhancing environmental quality. Notably, industrialization and urbanization become environmentally friendly when energy efficiency is integrated with these variables. Additionally, the NPQGC test supports the main results by confirming the Granger causality between the modelled series. Based on the outcomes, the study suggests that the integration of energy efficiency with industrialization and urbanization can significantly contribute to achieving a sustainable environment.

Prediction and quantification of nutrient concentrations in surface water has gained substantial attention during recent decades because excess nutrients released from agricultural and urban watersheds can significantly deteriorate surface water quality. Machine learning (ML) models are considered an effective tool for better understanding and characterization of nutrient release from agricultural fields to surface water. However, to date, no systematic investigations have examined the implementation of different classification and regression ML models in agricultural settings to predict nutrient concentrations in surface water using a group of input variables including climatological (e.g., precipitation), hydrological (e.g., stream flow) and field characteristics (i.e., land and crop use). In the current study, multiple classification (e.g., decision trees) and regression (e.g., regression trees) ML models were applied on a dataset pertaining to surface water quality in an agricultural watershed in southern Ontario, Canada (i.e., Upper Parkhill watershed). The target variables of these models were the nutrient concentrations in surface water including nitrate, total phosphorus, soluble reactive phosphorus, and total dissolved phosphorus. These target variables were predicted using physical and chemical water parameters of surface water (e.g., temperature and DO), climatological, hydrological, and field conditions as the input variables. The performance of these different models was assessed using various evaluation metrics such as classification accuracy (CA) and coefficient of determination (R²) for classification and regression models, respectively. In general, both the ensemble bagged trees and logistic regression (CA ≥ 0.72), and exponential Gaussian process regression (R²≥ 0.93) models were the optimal classification and regression ML algorithms, respectively, where they resulted in the highest prediction accuracy of the target variables. The insights and outcomes of the current study demonstrates that ML models can be employed to effectively predict and quantify the nutrient concentrations in surface waters to supplement field-collected monitoring data in agricultural watersheds, assisting in maintaining high quality of the available surface water resources.

Studies on the potential disruptions that future land use changes may have on trade-offs and synergies among ecosystem services (ESs) in the ecologically fragile region of hilly red soil region (HRSR) are still lacking. We employed multi-source observational data to project the land use patterns expected for the year 2035 in Jiangxi Province - a typical HRSR in China - across three specific scenarios: nature development (ND), economic development (ED), and ecological protection (EP). Through the integration of the InVEST model, correlation analysis, and geographically weighted regression methods, we evaluated habitat quality, soil conservation, water yield, and soil conservation, as well as the associated trade-offs/synergies among ESs. The results showed that the built-up land will continue to increase and occupy a large amount of cropland and woodland, resulting in a 0.79-1.96% reduction for the above four ESs under ND scenario. Under the ED scenario, the cropland and built-up land will increase by 2.95% and 12.00%, respectively, and most of them will convert from woodland, which will reduce ESs by 1.07-1.99%. Under the EP scenario, the expansion rate of built-up land will slow down and woodland will increase by 1.55%, leading to a 0.02-1.58% increase in ESs relative to the ED and ND scenarios. In addition, there were clear trade-offs observed in the ES pairs related to the water yield, while other ES pairs showed synergies. The proportions of counties that will experience changes in trade-off intensity, synergy intensity and the direction of trade-offs/synergies are expected to be 1-31%, 1-47% and 1-37%, respectively, from 2010 to 2035 under different scenarios. The study can provide valuable insights for ecological managers in HRSR in developing land use management strategies that optimize the mutual benefits of various ESs according to local conditions.

Information intervention has been verified to be effective in influencing individual behavior. Thus, can information intervention reverse the common discrepancy of high intention but poor action among the public about participating in environmental management? Clarifying the issue is critical to facilitating the public payment scheme for agricultural plastic waste management (APWM) to evolve from idea to reality, as well as harnessing potential contributions from the public to promote the sustainability of APWM. In light of these inquiries, the study seeks to reinforce the public's payment for APWM by employing an information strategy based on the Theory of Planned Behavior (TPB) and to verify the effect of information intervention on the respondents' willingness to pay (WTP) by a randomized controlled trial (RCT). Results showed that the public's WTP for APWM is generally malleable, with information targeting normative beliefs and control beliefs significantly increasing the WTP by CNY 307.2 and CNY 400.5, respectively. Findings imply that the public payment scheme for APWM is characterized by the high perception but weak social norm and lack of effective mechanism. Consequently, it is imperative to prioritize strengthening relevant norm and constructing public payment mechanism, thereby promoting multi-entity cooperation to enhance the APWM in sustainability.

The impact of landscape patterns on river water quality has been widely studied; however, it remains unclear which spatial scale has the greatest impact on water quality. Here, we analyzed the spatial scale and threshold impacts of the link between landscape metrics and water quality in a large-scale basin using the random forest (RF) model and nonparametric change point analysis (nCPA) method. The concentrations of nitrate nitrogen (NO₃^--N) and total nitrogen (TN) were comparatively high in winter and relatively low during spring and summer, whereas the total phosphorus (TP) concentrations were comparatively low during winter and summer and relatively high during spring. The R² values of the RF models at the sub-basin scale were generally higher than those at the riparian zone scale. Moreover, the R² of water quality modelling at the riparian zone scale demonstrated a declining tendency from a riparian zone 30 m-210 m wide in the majority of seasons. This shows that landscape metrics at the subbasin scale provide a better explanation for the variability in water quality than those at the riparian zone scale in the Hutuo River Basin. The results of the RF model indicated that landscape metrics of landscape configuration were more important in determining water quality during winter, whereas landscape metrics of landscape composition or physiography were more important in determining water quality during summer. Furthermore, several abrupt thresholds were estimated by nCPA; for example, the summertime slope abrupt threshold was 10.79° in the relationship between the slope and NO₃^--N. This study contributes to the understanding of the debate regarding the scale effects of landscape patterns on water quality, emphasizing the significance of the basin area and offering managers valuable insights into the control of non-point source pollution.