Environmental Impact Assessment Review最新文献

Rapid urbanization has spurred large-scale subway construction and led to the emergence of the prefabricated composite subway station (PCSSs), a novel station paradigm that integrates cast-in-situ (CIS) and prefabrication techniques, with significantly different greenhouse gas (GHG) emission boundaries than traditional stations. However, GHG emissions from PCSSs remain unclear. Under the current concept of sustainable construction, understanding the main GHG emission characteristics is essential to optimize the design and construction mode of PCSSs and facilitate their initial development. In this regard, this study innovatively takes the PCSS in Shenzhen Urban Rail Transit Phase 5 as a case study to analyze the PCSS's GHG emission efficiency from cradle-to-end of construction, and explore the key factors affecting GHG emissions. The results indicate that PCSSs reduce GHG emissions by 2.98 %–4.91% compared to traditional CIS stations, and interestingly, the PCSS's GHG emissions increase with the increase of prefabrication rate, which is primarily due to the nature of prefabricated composite technology. The GHG emissions in the production stage, transportation stage and construction stage accounted for 67.38%, 3.16% and 29.46%, respectively, among which the GHG emissions in the PCSS transportation stage are 3.04% higher than those in CIS stations. From a sub-project perspective, the enclosure structure (54.67%) and main structure (43.67%) are the main contributors to GHG emissions. Using formworks more than 40 times is effective for low-emission prefabricated composite construction. This study provides a systematic method for calculating GHG emissions of the novel PCSS model and offers industry practitioners scientific numerical analyses to enhance the PCSS's environmental benefits.

Environmental and socio-economic factors have impacts in different directions and magnitudes. This study identifies which factors added to carbon dioxide emissions (CO₂Es) in the productive sectors during 1990–2019 in Pakistan. Logarithmic mean Divisia Index and intensity analysis were applied to estimate the key factors: carbonization, substitution, transformation, energy intensity, social economic affluence, and population effects. The results show that (i) the intensity effect illustrates CO₂E growth due to huge fossil fuel consumption. The sectorial energy substitution provides significant variation except for agriculture and other govt. sectors that increased by 0.12 % and 0.86 %, showing an efficient transition. (ii) The transformation factor showed a huge impact on raising CO₂Es from 1990 to 1995 and then provided a decline in the transformation of primary energy to final energy average by 5.24 %, with significant changes in energy efficiency. (iii) Social affluence and population factors' effects contribute 7.83 % and 3.81 % to CO₂Es, causing significant growth. However, weak results of the substitution effect in mitigating CO₂Es show that Pakistan has much to do in the future and has great potential to lessen CO₂Es using multiple resources. Finally, the most imperative contributions to sectoral energy intensity are efficiency and measures in various sectors, while the reduction in industrial share impacts structural change. The study provides necessary intuitions into policy implications, highlighting an alternative mitigation technology to attain the targeted goals in a short time since the driving factors of CO₂Es change across the subsectors.

The psychological health of university students has become a focal point for researchers worldwide. The field of environmental psychology introduces the notion of a “restorative environment”, which can help promote psychological health, and increasing research has demonstrated that environmental characteristics play a significant role in psychological health. University common spaces enable a range of daily behavioural activities and play a critical role in the psychological development of university students. Consequently, it is necessary to evaluate the psychologically restorative effects on the environmental characteristics of university common spaces. In this study, 408 common spaces across 11 university campuses in Guangzhou were considered as research objects, and a deep learning full convolutional network (FCN) program was used to identify the environmental characteristic elements of these common spaces. Based on the identification results, five representative categories and 25 common space forms were selected for further quantitative analysis based on k-means clustering. The psychologically restorative effects of the environmental characteristics of the 25 common space forms were evaluated using the Perceived Restorativeness Scale (PRS). The findings indicate that (1) the environmental characteristics of the 25 common space forms can explain university students' psychologically restorative scores. Both architectural and landscape environmental characteristics directly and significantly impacted the psychologically restorative evaluations of university students. (2) Each common space's environmental characteristic elements contributed differently to the psychologically restorative evaluation. The psychological restoration of university students was primarily affected by three characteristic elements: the green view index, waterscape coverage, and openness of building enclosures. (3) Considering the environmental elements related to university students' psychological restoration and their respective magnitudes of influence, a predictive model of the psychologically restorative effects of university common spaces was successfully established. These results have practical applications for optimising the design of university common spaces and offer methodological suggestions and theoretical support for designing healthy environments on university campuses.

The government's policies on developing energy strategies have received much attention in empirical research. As such, fiscal and monetary policies can drive renewable energy. To this end, the current study proposes a new dimension in the empirical framework by regressing the fiscal and monetary policy variables on renewable energy under the moderating effect of political stability for the 71 “Belt and Road Initiative (BRI)” nations between 2000 and 2022. This research designs static and dynamic STIRPAT models to determine the effectiveness of lag periods. The results show that expansionary fiscal policy is instrumental in driving renewable energy development; in contrast, the contractionary monetary policy aligns with a reduction in renewable generation in the selected BRI countries. The moderating role of political stability in driving the monetary policy-renewable energy development and fiscal policy-renewable energy in these countries remain instrumental. The outcomes were reaffirmed by changing the estimator, testing the relationship with different fiscal and monetary policy indicators, and using the asymmetric analysis of the benchmark model to support the regression results. Finally, it discusses various policy proposals to the policymakers of BRI countries for practice.

Uncertainty is a central issue in environmental impact assessments (EIAs), as these assessments aim to predict future environmental consequences of proposed actions and development projects. Assessment, characterization, and management of such uncertainties have been discussed extensively in the literature on EIAs. Yet, calls are made for further work and improvements: There is a need for common terminology, principles and frameworks across specialized fields and applications. The present paper aims to contribute to this end by providing a risk science perspective on the understanding and treatment of uncertainties in EIAs. This perspective provides generic knowledge on concepts, principles, approaches, models and methods for assessing, characterizing, communicating and handling risk and uncertainties. An EIA can be seen as a study of future consequences and associated uncertainties, which in risk science captures the essential features of the risk concept. Following this logic, we analyze and illustrate how EIA can be seen as a special case of risk assessment. By considering the Norwegian EIA regulation, as well as key Norwegian EIA guidelines, we further demonstrate how different aspects of EIAs can be strengthened by adopting this perspective, including conceptual clarity and understanding. The paper concludes that integrating risk science concepts into established EIA practice is not straightforward, but further work on this integration can enhance both the EIA and risk fields.

Green finance reform and innovation pilot zones policy (GFRIPZ) is a vital financial system innovation in China to achieve sustainable development. This paper attempts to assess the policy impact of green finance reform on low-carbon energy transition (LCET) on a city-level. The entropy method is adopted to construct LCET comprehensive indexes including 284 prefecture-level cities and a multi-stage DID model is conducted to analyze how green finance reform affects LCET. This study reveals that green finance reform policy significantly accelerates LCET in pilot cities. Moreover, the spatial cross-regional mobility of green finance reform policy accelerating LCET is obvious. The spatial radiation effect of the policy varies with city characteristics of industrial and location. Furthermore, the pilot policy plays a promoting role and spatial radiation effect on LCET through the mechanism of technological progress and green financial product innovation. This paper not only enrich the relevant literature on the environmental impact assessment of GFRIPZ policy, but also provide a practical basis for financial policy innovation to help achieve regional LCET.

Green technology is becoming an important emerging field for achieving carbon neutrality. Green credit is a new financial tool of supporting low-carbon development. The motivation of this article is to investigate the impact and mechanism of green credit on green technology innovation. Most existing literature uses linear models to investigate green technologies, masking the possible nonlinear relationships between economic variables. Unlike existing literature, this article uses a data-driven nonparametric additive model to investigate the nonlinear impact of green credit on green technology innovation. The empirical findings show that green credit exerts a U-shaped impact on green technology innovation, signifying that the effect of green credit has shifted from less prominent in the early stages to prominent in the later stages. Heterogeneity analysis displays that the impact of green credit on green technology innovation in the provinces with different financial all are nonlinear. Further mechanism analysis demonstrates that both the banking market structure and corporate R&D investment generate a U-shaped impact on green technology innovation. Finally, this paper provides policy reference for promoting green credit development and achieving carbon neutrality.

Transforming agricultural systems to mitigate climate change challenges is one of China's most urgent tasks. The land, energy, food and waste nexus (LEFWN) in the agricultural sector not only improves resource efficiency but also has the potential to contribute to the mitigation of greenhouse gas (GHG). However, existing studies have not yet evaluated the potential impact of policy implementation on mitigating GHG within the LEFWN system, especially in identifying trade-offs and synergies. This study developed a system dynamics model (SDM) based on historical data from 2011 to 2020 in Jiangxi, China, incorporating positive and negative feedback mechanisms. This model generated five primary policy scenarios and eleven derived scenarios. The findings show that the SDM highlighted dynamic characteristics and nonlinear relationships within the LEFWN systems. The integrated scenario, which includes bioenergy development, land use policies, and the substitution of organic fertilizers for chemical fertilizers, can potentially decrease GHG emissions by up to about 14.20 %. Significant trade-offs exist between food security policies and other policies, depending on the strategic objectives pertaining to land, energy, or waste management. Policymakers can benefit from a comprehensive understanding of the dynamic interplay between agricultural resources and assessing the effectiveness and utility of different strategies. Assessing the environmental impacts of agricultural transformation can offer empirical evidence to advocate for promoting low-carbon sustainable agriculture and provide insights for developing countries addressing climate change challenges.

Climate change adaptation, CCA henceforth, is nowadays a shared concern, deeply investigated and advocated by international research and political organisations. However, both CCA implementation and its monitoring and evaluation (M&E) are challenges yet to be properly addressed. From a spatial planning perspective, local plans are the land-use-oriented tools with the highest potential to enhance CCA operativity. Strategic Environmental Assessment (SEA) is also acknowledged to be a key instrument to integrate climate change concerns and hence, to monitor and evaluate climate change (CC) risks and CCA efforts. This study addresses two hypotheses, i.e., i) indicators included in SEAs' spatial plans may be used at the service of CCA M&E, ii) the full extent of indicators can be captured by multi-level planning analyses. To this aim, this study provides an in-depth analysis, through a multi-step systematic categorization, of the indicators used within the SEA of regional and municipal plans in the Friuli Venezia Giulia Region (Italy). This study brings novelty in the SEA research field by bridging the climate risk theoretical principles to the methodological approach for analysing SEAs' indicators, which are classified within the risk function frame. Key insights come from the metrics, the indicators' explicitness for CCA, and the indicators' extent into the climate risk function. Finally, the paper paves the way for further research of CC- and CCA-related indicators in both spatial planning and other public sectors to support CCA mainstreaming through SEAs.

Unbalancing ecological supply-demand is an obstacle to sustainable development in the Beijing-Tianjin-Hebei urban agglomeration (BTHUA), which can be efficiently addressed through regional ecological compensation. However, the driving mechanism of this unbalance is unclear, and the determination of ecological compensation standard (ECS) is not unified. This study uses the ecological footprint (EF) model and integrated inversion model to complete the intelligent evaluation and prediction of ecological supply-demand performance in the BTHUA. The extended nonlinear STIRPAT model and geographic detector are integrated to identify the driving mechanism of EF and its spatial differentiation. ECS at different spatiotemporal scales are then given with consideration of ecological service value and Monte Carlo simulation. Results reveal that EF of the BTHUA exhibits a downward trend, especially under the sustainable scenario (SSP1), falling by 16.85 %. But all cities would be still in unsafety states by 2035, with an average EF value of 6.210 hm²/cap, which is over 27 times greater than its EC. The spatial distribution of EF differs significantly, and high-value areas gradually migrate to the north. The variation of EF is primarily influenced by industrial structure, with population factors and environmental factors following. Per capita GDP is a key factor causing spatial differentiation of EF. The key driving factors and their explanatory powers on EF vary across cities, and the interaction of multiple factors affects EF more than a single in the BTHUA. Past-to-future ECS of the BTHUA shows a decreasing trend, with high-value areas distributed in the pivotal cities for economic development. A total of 19.13 × 10¹⁰ CNY needs to be paid to compensate for ecological damage of the BTHUA. Uncertainty analysis shows that ECS is extremely sensitive to grassland footprint in most cities. Furthermore, environmental footprints as well as system dynamics considering multiple factors are still required for comprehensively evaluating the ecological environment quality and further exploring ecological compensation evaluation framework with incorporating various ecological service functions such as carbon sinks in the BTHUA. Findings can facilitate improving regional sustainability and provide a valid approach to determine ECS for the BTHUA and similar regions worldwide.