Search

The reliance solely on the government or enterprises to promote climate governance is contingent upon the vested interests of economic entities and the regulatory bodies' efficiency in governance. Can the model of government-enterprise green collaborative governance evolve into a long-term mechanism for addressing the climate crisis and achieving the goals of sustainable development? By crawling data on public-private partnerships (PPP), employing ChatGPT to identify green PPP projects, and building a generalized difference-in-differences framework based on the Guidance on Building a Green Financial System issued in 2016, this present study investigates whether the involvement of private capital in government-led environmental and climate governance can effectively facilitate government-enterprise green collaborative governance, thereby mitigating urban carbon emissions. The study finds government-enterprise green collaborative governance can significantly reduce urban carbon emissions. The conclusion remains valid even after several rounds of robustness tests, including removing the influence of pertinent climate policies, adjusting the settings of independent and dependent variables, and removing self-selection issues. Heterogeneity tests show, on the first hand, the carbon emission reduction effect of government-enterprise green collaborative governance differs due to the differences in the characteristics of green PPP(Pubic-private partnership) projects such as project return mechanism, project investment volume, and project cooperation term; on the other hand, the carbon emission reduction effect also shows heterogeneity with various urban characteristics such as geographical location, city type and city size. Mechanism tests indicate government-enterprise green collaborative governance affects urban carbon emissions mainly through structural effects, technological effects and co-investment effects. This paper offers a valuable framework for effectively promoting environmental and climate co-governance between governmental bodies and enterprises, while enhancing the market's role in resource benefit allocation within climate governance to mitigate the risks associated with climate change. • This paper first verifies the effectiveness of the green PPP model in carbon emission control. • The use of ChatGPT to identify green PPP projects, which provides an innovative method for data processing. • The carbon reduction effect varies with the characteristics of green PPP projects and cities. • This paper offers a valuable framework for effectively promoting climate co-governance between governmental and enterprises. [ABSTRACT FROM AUTHOR]

Anaerobic ammonium oxidation has been considered as an environmental-friendly and energy-efficient biological nitrogen removal (BNR) technology. Recently, new reaction pathway for ammonium oxidation under anaerobic condition had been discovered. In addition to nitrite, iron trivalent, sulfate, manganese and electrons from electrode might be potential electron acceptors for ammonium oxidation, which can be coupled to traditional BNR process for wastewater treatment. In this paper, the pathway and mechanism for ammonium oxidation with various electron acceptors under anaerobic condition is studied comprehensively, and the research progress of potentially functional microbes is summarized. The potential application of various electron acceptors for ammonium oxidation in wastewater is addressed, and the N 2 O emission during nitrogen removal is also discussed, which was important greenhouse gas for global climate change. The problems remained unclear for ammonium oxidation by multi-electron acceptors and potential interactions are also discussed in this review. [Display omitted] [ABSTRACT FROM AUTHOR]

Current urban and transport planning practices have significant negative health, environmental, social and economic impacts in most cities. New urban development models and policies are needed to reduce these negative impacts. The Superblock model is one such innovative urban model that can significantly reduce these negative impacts through reshaping public spaces into more diverse uses such as increase in green space, infrastructure supporting social contacts and physical activity, and through prioritization of active mobility and public transport, thereby reducing air pollution, noise and urban heat island effects. This paper reviews key aspects of the Superblock model, its implementation and initial evaluations in Barcelona and the potential international uptake of the model in Europe and globally, focusing on environmental, climate, lifestyle, liveability and health aspects. We used a narrative meta-review approach and PubMed and Google scholar databases were searched using specific terms. The implementation of the Super block model in Barcelona is slow, but with initial improvement in, for example, environmental, lifestyle, liveability and health indicators, although not so consistently. When applied on a large scale, the implementation of the Superblock model is not only likely to result in better environmental conditions, health and wellbeing, but can also contribute to the fight against the climate crisis. There is a need for further expansion of the program and further evaluation of its impacts and answers to related concerns, such as environmental equity and gentrification, traffic and related environmental exposure displacement. The implementation of the Superblock model gained a growing international reputation and variations of it are being planned or implemented in cities worldwide. Initial modelling exercises showed that it could be implemented in large parts of many cities. The Superblock model is an innovative urban model that addresses environmental, climate, liveability and health concerns in cities. Adapted versions of the Barcelona Superblock model are being implemented in cities around Europe and further implementation, monitoring and evaluation are encouraged. The Superblock model can be considered an important public health intervention that will reduce mortality and morbidity and generate cost savings for health and other sectors. [ABSTRACT FROM AUTHOR]

Estuaries are dynamic environments which are driven by various natural processes like river discharge, tides, waves, influx of saline water and sediments, etc. These ecosystems are the most sensitive to sea level rise and fluctuations in river discharge associated with climate change. A direct response of sea level rise and river discharge can be observed in the water level of estuaries. However, existing models have not considered these parameters for forecasting water level. This paper focuses on developing a water level forecast model for the Chikugo River estuary in Japan using Nonlinear Autoregressive with Exogenous inputs (NARX Model). NARX neural network was used to do the one-step-ahead prediction of water level considering the various parameters that can very well be influenced by climate change: previous water level, river discharge, and salinity. Accordingly, three models were developed: (i) Model I considering previous water level; (ii) Model II additionally considering river discharge; and (iii) Model III additionally considering salinity. All the models showed appreciable performance in forecasting the water level. Model III had the best correlation with the water level with a cross-correlation value of 0.6030, while the river discharge had only a cross-correlation of 0.1113 indicating that the Chikugo River estuary is tide-dominated. The model was trained using different combinations of available data - previous water level, river discharge, and salinity. Cross-correlation results showed a better correlation between water level and salinity than various other combinations trained. Therefore, tidal intrusion influences the water level in the estuary, thereby depicting that sea level rise can affect the water level, and its influence can be well predicted by the developed model. The water level significantly affects the flora and fauna and the predictability of future estuarine floods can help in taking necessary mitigation strategies. • Water Level of Chikugo estuary forecasted using NARX model. • Analyzed the influence of previous water level, discharge and salinity. • Water level has more cross-correlation with salinity than discharge. • Forecasted the water level for various river discharge and salinity. • Model capable to analyse the influence of climate change on water level. [ABSTRACT FROM AUTHOR]