Abstract This perspective uses five recently published data points to explore why local action on urban nature in Africa is important for the world, and what we can all do about it.

Abstract Climate change is expected to increase the severity of hurricanes and tropical storms, posing significant risks to the electricity grid. These include downed power lines, damaged solar panels, and impaired wind turbines from high winds. New York (NY) and New Jersey (NJ) are not spared from these vulnerabilities and must strengthen their infrastructure and mitigate social and technical impacts. Clean energy mandates, such as NJ’s Executive Orders No. 315 and 307 (100% clean energy by 2035 and 11 GW of offshore wind by 2040), and NY’s Executive Order No. 166 (40% emissions reduction by 2030), add urgency to ensuring grid resilience under extreme weather. This study demonstrates the power system cyclone impact model (PCIM), used alongside the GenX electricity system planning tool, to assess grid resilience under hurricane-induced high wind speeds in the NY and NJ region. Results reveal that onshore and offshore wind could contribute additional power during storms, provided transmission and storage systems remain operational. This output helps offset outages elsewhere in the grid across all storm categories. In contrast, solar emerges as a vulnerability due to combined impacts from wind stress and cloud cover, significantly reducing generation during and after storms. Thermal generators show the lowest failure rates, though this may partly reflect current model limitations, as only wind stress and cloud cover are considered, excluding hazards like flooding. Non-served energy costs vary with electricity demand and fluctuations in wind and solar output. July stands out as the most vulnerable month, due to high demand and limited wind generation, leading to higher non-served energy. This research provides a first step toward understanding storm-related grid resilience in NJ and NY. The PCIM is designed to be generalizable, with future work focused on expanding its scope to include additional hazards like storm surge and flooding, and more storm-prone regions.

Abstract Despite major progress in electricity access and renewable energy deployment over recent decades, Senegal continues to face challenges in achieving universal rural electrification, a country where 55% of the rural population lack access. A core issue is the absence of a coherent regulatory framework for rural electrification. Initiatives are fragmented, often pursue competing priorities, and involve a complexity of different actors and institutions. Within this context, mini grids have been promoted by donors and international organisations as a promising solution, and Senegal was once considered a regional leader in this sector. Today, most planned projects are solar or solar–diesel hybrid, as compared to the original diesel-generated systems. However, not only are a significant portion of the previously installed projects presumed to no longer be operational, but their actual and projected contribution to Senegal’s rural electrification rate is relatively small. In the case of mini grids more specifically, there have been inconsistencies with regards to licensing, tariff-setting and arrangements for the arrival of the main grid. Processes and standards for their installation, operation, maintenance and ownership have been carried out in a somewhat haphazard way. Despite the introduction of regulation intended to support an increased role for the private sector in the electricity sector more generally, most mini grids that have been developed to date have been government-owned and donor-funded. Private sector involvement has been largely confined to engineering, procurement, and construction, and operation and maintenance. With this in mind, this paper critically examines the political, institutional, and regulatory barriers to rural electrification in Senegal. It highlights the tension between grid extension and the introduction of decentralised/off-grid systems, finding a significant mismatch between donor ideals and expectations on the one hand and the preferences of the state utility, as well as national and local governments on the other.

Abstract Global energy consumption has significantly increased as a result of the rise in appliance and equipment usage, which has been driven by technological improvements and economic expansion, particularly noticeable in the infrastructure and building industries. Among these, residential construction emerges as a prominent energy consumer in infrastructure development. Therefore, architects and engineers must prioritize the adoption of energy-efficient strategies in both planning and execution to create buildings that achieve net-zero or nearly-zero energy consumption levels. This study aims to reduce energy usage in unconditioned residential buildings by employing a metamodel-based design optimization approach, while taking into account indoor thermal comfort temperature (ITCT) and useful daylight illuminance (UDI) as constraints. Here, a parametric model is created utilizing four architectural design variables: orientation, window-to-wall ratio, shading depth, and shading angle that generates a large number of options for the analysis of the building’s energy consumption. The outcomes of the case study demonstrate a significant decrease of 34.63 % in energy consumption compared to the reference design, achieved through the optimal selection of design variables.

Abstract This focus issue on community energy and infrastructure resilience compiles six contribution that seek to put into context projects that aim to put people at the core of energy development. The promises of community energy are multifold, from facilitating the democratization of energy systems to allowing for the development of decentralized and off-grid networks, which may accelerate the adoption of renewables. However, community energy also faces challenges. In this case, the special issue focused on challenges related to infrastructure resilience, whether they are linked to building resilience in community energy projects or to the contributions that community energy projects make to advance sustainability transitions. This focus issue makes five key thematic contributions: the importance of community engagement in building resilience, analytical tools to understand the multi-dimensional nature of infrastructure resilience, the need to incorporate place-based concerns into policy, the development of tools to evaluate different aspects of resilience, and the explicit consideration of gender equality and social inclusion to facilitate project sustainability.

Abstract The Amazon region faces the critical challenge of balancing economic development with urgent environmental preservation. This study leverages advanced geospatial analytics and machine learning to map and analyze both physical and digital connectivity across the Amazon. We integrate heterogeneous datasets—including road networks, navigable waterways, building footprints, internet, and electricity access—within a uniform H3 spatial indexing system. Our analysis exposes stark infrastructure disparities: only 33% of the region has adequate road access. While 51% is served by navigable waterways, this infrastructure remains critically underutilized, with just 621 docks identified. Digital connectivity is similarly fragmented, with 42% of the area lacking sufficient data for assessment. K-means clustering reveals four distinct connectivity profiles: high-connectivity urban hubs (5%), medium-connectivity rural zones (60%), low-connectivity pristine forests (26%), and isolated remote settlements (9%). SHAP analysis identifies transportation accessibility as the primary driver of development patterns, followed by internet access and proximity to essential services. Utilizing these insights, we developed a machine learning-derived Infrastructure Provision Index, identifying 12.4% of the Amazon as critical development zones requiring targeted intervention. This comprehensive mapping framework provides an essential evidence base to guide sustainable development strategies that enhance livelihoods while safeguarding the Amazon's ecological integrity.

Abstract Prospective climate risk assessments for climate change adaptation and emergency management rely on reliable, accurate data about the built environment. Yet, urban areas in developing countries are growing rapidly, so data sources and methods that measure urban growth in a timely manner are critical. However, current methods that leverage satellite data and machine learning to produce building footprint datasets are prone to biases correlated with urban risk due to limited training data across different continents and types of urban areas, as well as challenges in interpreting satellite imagery across different urban forms. In this paper, we aim to improve the reliability of building footprint data across urban forms through the integration of limited local data using Hidden Markov Models. We present three key contributions: (1) an urban climate risk assessment framework to evaluate datasets derived from deep machine learning models and satellite imagery across urban forms; (2) a method for processing probabilistic outputs of aggregate building footprint data to account for uncertainty among risk classes; (3) a Hidden Markov model method to calibrate convolutional neural network outputs in post-processing with small local datasets to overcome biases critical to climate risk assessments and downstream management decisions. In a case study of Kigali, Rwanda, we show that Hidden Markov models calibrated on data from similar local climate zones (LCZs) can improve the MSE of built area percent at a block scale from the current building footprint models at 6.8% down to 2.4%. Furthermore, these models reduce standard deviation in performance of estimation of percent built area across LCZs from 6.6% to 2.6%, reducing the variability in the reliability of built area estimates in high-risk LCZs.

Abstract The offshore wind energy (OWE) sector is experiencing rapid global growth, with ambitious plans to scale up renewable energy capacity significantly. While this expansion is vital for mitigating climate change, ensuring the resilience of OWE infrastructure in the face of extreme weather and climatic events exacerbated by climate change remains a critical yet often overlooked aspect of the current literature. The main objective of this topical review is twofold. First, we provide a critical synthesis of related literature to outline how key aspects of climate change, such as rising ocean temperatures, shifting wind patterns, and intensifying storms, may affect the performance, maintenance needs, and structural integrity of OWE infrastructure. Second, we perform a global spatial analysis that overlays projections of climate hazards under the shared socioeconomic pathways with datasets of current and planned OWE installations. This approach allows us to identify geographic hotspots where climate-related stressors intersect with major OWE development zones, highlighting areas that require targeted resilience strategies. This understanding is essential for developing proactive strategies to ensure the long-term viability and resiliency of current and future OWE infrastructure.