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Abstract Many cities are experiencing increases in extreme heat because of global temperature rise combined with the urban heat island effect. The heterogeneity of urban morphology also leads to fine-scale variability in potential for heat exposure. Yet, how this rise in temperature and local variability together impacts urban residents differently at exposure-relevant scales is still not clear. Here we map the Universal Thermal Climate Index, a more complete indicator of human heat stress at an unprecedentedly fine spatial resolution (1 m), for 14 major cities in the United States using urban microclimate modeling. We examined the different heat exposure levels across different socioeconomic and racial/ethnic groups in these cities, finding that income level is most consistently associated with heat stress. We further conducted scenario simulations for a hypothetical 1 °C increase of air temperature in all cities. Results show that a 1 °C increase would have a substantial impact on human heat stress, with impacts that differ across cities. The results of this study can help us better evaluate the impact of extreme heat on urban residents at decision-relevant scales. 

Targets can distort competition in favor of incumbent firms 

Abstract Although the potential for cities and regions to contribute to global mitigation efforts is widely acknowledged, there is little evidence on the effectiveness of subnational mitigation strategies. Here we address this gap through a systematic review of 234 quantitative mitigation case studies. We use a meta-analytical approach to estimate expected greenhouse gas emissions reductions from 12 categories of mitigation strategies. We find that strategies related to land use and development, circular economy, and waste management are most effective and reliable for reducing emissions. The results demonstrate that cities and regions are taking widespread action to reduce emissions. However, we find misalignment between the strategies that policymakers and researchers focus on, compared to those with the highest expected impacts. The results inform climate action planning at the city and regional level and the evaluation of subnational climate targets. 

The Regenerative Finance (ReFi) movement aims to fundamentally transform the governance of global common pool resources (CPRs), such as the atmosphere, which are being degraded despite international efforts. The ReFi movement seeks to achieve this by utilizing digital monitoring, reporting, and verification (D-MRV); tokenization of assets; and decentralized governance approaches. However, there is currently a lack of a clear path forward to create and implement models that actually drive the “Re-” in ReFi beyond perpetuating the existing extractive economics and toward actual regeneration. In addition, ReFi suffers from growing pains, lacking a common interoperability framework and definition for determining what a ReFi project is and how the individual components align toward the grand ambition. This paper provides a definition of the ReFi stack of interconnected components and examines how it can address limitations in climate change accounting, finance and markets, and governance. The authors also examine the theory of regenerative economics and CPRs to encourage further discussions and advancements in the ReFi space. The crucial question remains if and how ReFi can drive a change in paradigm toward the effective regeneration of global CPRs. 

Abstract Although cities have risen to prominence as climate actors, emissions’ data scarcity has been the primary challenge to evaluating their performance. Here we develop a scalable, replicable machine learning approach for evaluating the mitigation performance for nearly all local administrative areas in Europe from 2001-2018. By combining publicly available, spatially explicit environmental and socio-economic data with self-reported emissions data from European cities, we predict annual carbon dioxide emissions to explore trends in city-scale mitigation performance. We find that European cities participating in transnational climate initiatives have likely decreased emissions since 2001, with slightly more than half likely to have achieved their 2020 emissions reduction target. Cities who report emissions data are more likely to have achieved greater reductions than those who fail to report any data. Despite its limitations, our model provides a replicable, scalable starting point for understanding city-level climate emissions mitigation performance. 

The Paris Agreement’s decentralized and bottom-up approach to climate action poses an enormous accounting challenge by substantially increasing the number of heterogeneous national, sub-national, and non-state actors. Current legacy climate accounting systems and mechanisms are insufficient to avoid information asymmetry and double-counting due to actor heterogeneity and fragmentation. This paper presents a nested climate accounting architecture that integrates several innovative digital technologies, such as Distributed Ledger Technology, Internet of Things, Machine Learning, and concepts such as nested accounting and decentralized identifiers to improve interoperability across accounting systems. Such an architecture can enhance capacity building and technology transfer to the Global South by creating innovation groups, increasing scalability of accounting solutions that can lead to leapfrogging into innovative systems designs, and improving inclusiveness.