This study outlines a two-stage sequential optimization framework in which the first stage optimizes the social welfare of the agricultural and bioenergy sectors, and the second stage incorporates the land-use change from bioenergy development to examine the regional solar energy capacity.

Researchers assess the critical mineral demand required to meet sustainability goals using a bottom-up, scenario-based approach and examine how mineral bottlenecks affect sub-technology choices.

Using learning curve models and counterfactual inference, esearchers quantify their respective contributions to the decline in total installed cost of wind and PV power.

This chapter supports UN SDGs 7, 11, and 13 by offering a structure for urban policy-makers to create sustainable and energy-efficient cities by means of combined urban design, creative policy development, and environmentally friendly transportation options.

The article examines the complex systemic risks and vulnerabilities arising from the transition to renewable energy and climate change, emphasizing the need for integrated risk management and collaboration to enhance resilience for first responders and policymakers.

The article proposes a predictive control strategy for Plug-in Hybrid Electric Vehicles (PHEVs) that utilizes route information from digital map providers to enable electric driving within zero-emission zones. The study develops a supervisory control with Dynamic Programming in the cloud to calculate an optimal State of Charge (SOC) trajectory, which is then used to guide the rule-based controller in the vehicle.