• Energy Research
• Closed Access close

Summary The integration of renewable generation and the electrification of heating and transportation are critical for the sustainable energy transition toward net-zero greenhouse gas emissions. These changes require the large-scale adoption of distributed energy resources (DERs). Peer-to-peer (P2P) energy trading has gained attention as a new approach for incentivizing the uptake and coordination of DERs, with advantages for computational scalability, prosumer autonomy, and market competitiveness. However, major unresolved challenges remain for scaling out P2P trading, including enforcing network constraints, managing uncertainty, and mediating transmission and distribution conflicts. Here, we propose a novel multiscale design framework for P2P trading, with inter-platform coordination mechanisms to align local transactions with system-level requirements, and analytical tools to enhance long-term planning and investment decisions by accounting for forecast real-time operation. By integrating P2P trading into planning and operation across spatial and temporal scales, the adoption of large-scale DERs is tenable and can create economic, environmental, and social co-benefits.

The gap between actual carbon prices and those required to achieve ambitious climate change mitigation could be closed by enhancing the public acceptability of carbon pricing through appropriate use of the revenues raised. In this Perspective, we synthesize findings regarding the optimal use of carbon revenues from both traditional economic analyses and studies in behavioural and political science that are focused on public acceptability. We then compare real-world carbon pricing regimes with theoretical insights on distributional fairness, revenue salience, political trust and policy stability. We argue that traditional economic lessons on efficiency and equity are subsidiary to the primary challenge of garnering greater political acceptability and make recommendations for enhancing political support through appropriate revenue uses in different economic and political circumstances. Ambitious carbon pricing reform is needed to meet climate targets. This Perspective argues that effective revenue recycling schemes should prioritize behavioural considerations that are aimed at achieving greater political acceptance.

Photovoltaic (PV) solar energy generating capacity has grown by 41 per cent per year since 20091. Energy system projections that mitigate climate change and aid universal energy access show a nearly ten-fold increase in PV solar energy generating capacity by 20402,3. Geospatial data describing the energy system are required to manage generation intermittency, mitigate climate change risks, and identify trade-offs with biodiversity, conservation and land protection priorities caused by the land-use and land-cover change necessary for PV deployment. Currently available inventories of solar generating capacity cannot fully address these needs1-9. Here we provide a global inventory of commercial-, industrial- and utility-scale PV installations (that is, PV generating stations in excess of 10 kilowatts nameplate capacity) by using a longitudinal corpus of remote sensing imagery, machine learning and a large cloud computation infrastructure. We locate and verify 68,661 facilities, an increase of 432 per cent (in number of facilities) on previously available asset-level data. With the help of a hand-labelled test set, we estimate global installed generating capacity to be 423 gigawatts (-75/+77 gigawatts) at the end of 2018. Enrichment of our dataset with estimates of facility installation date, historic land-cover classification and proximity to vulnerable areas allows us to show that most of the PV solar energy facilities are sited on cropland, followed by aridlands and grassland. Our inventory could aid PV delivery aligned with the Sustainable Development Goals.

Modelling the economics of climate change is daunting. Many existing methodologies from social and physical sciences need to be deployed, and new modelling techniques and ideas still need to be developed. Existing bread-and-butter micro- and macroeconomic tools, such as the expected utility framework, market equilibrium concepts and representative agent assumptions, are far from adequate. Four key issues—along with several others—remain inadequately addressed by economic models of climate change, namely: (1) uncertainty, (2) aggregation, heterogeneity and distributional implications (3) technological change, and most of all, (4) realistic damage functions for the economic impact of the physical consequences of climate change. This paper assesses the main shortcomings of two generations of climate-energy-economic models and proposes that a new wave of models need to be developed to tackle these four challenges. This paper then examines two potential candidate approaches—dynamic stochastic general equilibrium (DSGE) models and agent-based models (ABM). The successful use of agent-based models in other areas, such as in modelling the financial system, housing markets and technological progress suggests its potential applicability to better modelling the economics of climate change.