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Low-carbon transitions demand long-term systemic transformations and meaningful societal engagement. Most approaches to engaging society with energy and climate change fail to address the systemic nature of this challenge, focusing on discrete forms of participation in specific parts of wider systems. Our systemic approach combines comparative case mapping of diverse public engagements across energy systems with participatory distributed deliberative mapping of energy system futures. We show how UK public participation with energy is more diverse than dominant approaches posit. Attending to these more varied models of participation opens up citizen and specialist views, values and visions of sustainable energy transitions, revealing support for more distributed energy system futures that recognize the roles of society. Going beyond narrow, discrete understandings of communication and public engagement towards systemic approaches to mapping participation can provide plural and robust forms of social intelligence needed to govern low-carbon transitions in more socially responsive, just and responsible ways. Chilvers et al. present a systemic approach to participation that combines mapping diverse public engagements across a national energy system with a distributed deliberative mapping process involving citizens and specialists, which shows support for more distributed and inclusive energy system futures.

The demographic–transport nexus is central to regional integration, but remains insufficiently studied in rapidly urbanizing contexts. Taking China’s Pearl River Delta (PRD) as a representative megaregion, this study uses panel data from nine PRD cities spanning 1990 to 2020. We construct an entropy-weighted indicator system and apply a coupling–coordination model in combination with a panal data regression to trace the co-evolution of population and transport systems and identify their driving forces. Findings reveal that: (1) the regional coupling-coordination index rose from 0.21 to 0.54 but still shows a clear core–periphery gradient—Guangzhou and Shenzhen already display high coordination, whereas ZhaoQing and Jiangmen lag behind; (2) economic growth, a consumption-oriented economic structure and technological progress significantly enhance coordination; (3) the 2009 PRD Master Plan mainly benefits core cities, with limited policy spill-overs; (4) medical-service provision improves coordination, while basic-education supply is not significant, highlighting service-level disparities. We recommend strengthening peripheral inter-city corridors, building 30- to 60-minute commuting rings, and linking transport investment to real-time coupling metrics and coordinated industry relocation to advance the region toward higher-level integration.

Redlining was a racially discriminatory housing policy established by the federal government’s Home Owners’ Loan Corporation (HOLC) during the 1930s. For decades, redlining limited access to homeownership and wealth creation among racial minorities, contributing to a host of adverse social outcomes, including high unemployment, poverty, and residential vacancy, that persist today. While the multigenerational socioeconomic impacts of redlining are increasingly understood, the impacts on urban environments and ecosystems remain unclear. To begin to address this gap, we investigated how the HOLC policy administered 80 years ago may relate to present-day tree canopy at the neighborhood level. Urban trees provide many ecosystem services, mitigate the urban heat island effect, and may improve quality of life in cities. In our prior research in Baltimore, MD, we discovered that redlining policy influenced the location and allocation of trees and parks. Our analysis of 37 metropolitan areas here shows that areas formerly graded D, which were mostly inhabited by racial and ethnic minorities, have on average ~23% tree canopy cover today. Areas formerly graded A, characterized by U.S.-born white populations living in newer housing stock, had nearly twice as much tree canopy (~43%). Results are consistent across small and large metropolitan regions. The ranking system used by Home Owners’ Loan Corporation to assess loan risk in the 1930s parallels the rank order of average percent tree canopy cover today.

Human activities, such as research, innovation and industry, concentrate disproportionately in large cities. The ten most innovative cities in the United States account for 23% of the national population, but for 48% of its patents and 33% of its gross domestic product. But why has human activity become increasingly concentrated? Here we use data on scientific papers, patents, employment and gross domestic product, for 353 metropolitan areas in the United States, to show that the spatial concentration of productive activities increases with their complexity. Complex economic activities, such as biotechnology, neurobiology and semiconductors, concentrate disproportionately in a few large cities compared to less--complex activities, such as apparel or paper manufacturing. We use multiple proxies to measure the complexity of activities, finding that complexity explains from 40% to 80% of the variance in urban concentration of occupations, industries, scientific fields and technologies. Using historical patent data, we show that the spatial concentration of cutting-edge technologies has increased since 1850, suggesting a reinforcing cycle between the increase in the complexity of activities and urbanization. These findings suggest that the growth of spatial inequality may be connected to the increasing complexity of the economy. Balland et al. use data on scientific papers, patents, employment and GDP for 353 metropolitan areas in the United States to show that economic complexity drives the spatial concentration of productive activities in large cities.

UK policy on decentralized energy has shifted from community energy to local energy. This signals reduced support for grassroots, citizen-led action in favour of institutional partnerships and company-led investments, which puts at risk the urgent, long-term social and technological transformations required in a climate emergency.

Quantifying the topological similarities of different parts of urban road networks enables us to understand urban growth patterns. Although conventional statistics provide useful information about the characteristics of either a single node’s direct neighbours or the entire network, such metrics fail to measure the similarities of subnetworks or capture local, indirect neighbourhood relationships. Here we propose a graph-based machine learning method to quantify the spatial homogeneity of subnetworks. We apply the method to 11,790 urban road networks across 30 cities worldwide to measure the spatial homogeneity of road networks within each city and across different cities. We find that intracity spatial homogeneity is highly associated with socioeconomic status indicators such as gross domestic product and population growth. Moreover, intercity spatial homogeneity values obtained by transferring the model across different cities reveal the intercity similarity of urban network structures originating in Europe, passed on to cities in the United States and Asia. The socioeconomic development and intercity similarity revealed using our method can be leveraged to understand and transfer insights between cities. It also enables us to address urban policy challenges including network planning in rapidly urbanizing areas and regional inequality. The spatial homogeneity of urban road networks can be quantified in a fine-grained manner with graph neural networks. This method is studied across 11,790 inner-city road networks around the world and can be used to study socioeconomic development and help with urban planning.

The acceleration of global urbanization has increased the frequency of the urban heat island (UHI) effect and heatwaves, which seriously endanger human health. We used Shenzhen as a case study to examine the daytime and nighttime differences in UHI intensity (UHII), considering different local climate zones (LCZs) and sea–land breezes. The diurnal UHII was >3 °C for 52% of the study period, whereas the nocturnal UHII was >3 °C for only 26% of the study period. The average diurnal and nocturnal building-type UHII values were 2.77 and 1.11 °C higher than those of the natural type, respectively. Sea breezes alleviated the UHI effect with a linear correlation coefficient of −0.68601 between them. Moreover, diurnal and nocturnal UHII showed differences across different gradients, which can help guide urban planning.

Energy justice frameworks overlook how space creates inequality. Whilst examining distribution, participation and recognition, they miss that geography itself is a mechanism of injustice. Land classification dispossesses Indian pastoralists. Boundary drawing erases Brazilian fishing communities. Infrastructure placement violates Sámi territorial rights in Norway. These spatial strategies transform renewable energy into tools of exclusion. Just transitions demand interrogating how distance, territory and infrastructure actively produce injustice, not merely host it.

Future projection of global land surface temperature often emphasizes climate change while neglecting urbanization. Yet, urbanization-induced warming strongly influences heatwave-related health risks and energy demands. Here, we developed a 1-km resolution global land surface temperature dataset for 2020–2100 at five-year intervals, combing climate change-induced global warming and urbanization-driven local warming, which were estimated using multi-model ensemble projections, and a dynamic regression model linking impervious surface area and local temperature, respectively. Our dataset aligns closely with satellite observations, showing high spatial and temporal consistency. By 2100, urbanization contributes an average local warming of 0.1 °C, with approximately 10–16% of urban areas experience extreme warming exceeding 1 °C. Urban areas remain warmer than the global mean, whereas their warming rates are 0.5–8% lower than the global average under all scenarios. The derived dataset enables improved assessments of urban heat risks assessments and supports climate-resilient urban planning. By 2100, urbanization contributes an average local warming of 0.1 degree centigrade, and up to 16 percent of urban areas experience extreme warming exceeding 1 degree centigrade, according to an analysis that combines climate model projections and a dynamic regression model.

Segregation across social groups is an enduring feature of nearly all human societies and is associated with numerous social maladies. In many countries, reports of growing geographic political polarization raise concerns about the stability of democratic governance. Here, using advances in spatial data computation, we measure individual partisan segregation by calculating the local residential segregation of every registered voter in the United States, creating a spatially weighted measure for more than 180 million individuals. With these data, we present evidence of extensive partisan segregation in the country. A large proportion of voters live with virtually no exposure to voters from the other party in their residential environment. Such high levels of partisan isolation can be found across a range of places and densities and are distinct from racial and ethnic segregation. Moreover, Democrats and Republicans living in the same city, or even the same neighbourhood, are segregated by party. Enos and Brown find that voters in US are highly spatially segregated by party. Republicans and Democrats tend to segregate even when living in the same neighbourhoods, and this segregation persists in both urban and rural areas.