In the United States, PMCs are a solution growing in popularity, but there remains uncertainty regarding best practices and guidance for cities interested in assessing its feasibility. This paper aims to fill that gap and encourage cities to assess feasibility of PMCs in their local context.

This paper identifies the largest barriers that prevent ride-hailing drivers from accessing EVs and analyzes ways that governments, industry and other stakeholders can tackle those barriers. It includes city scorecards that evaluate 10 U.S., European and Canadian cities on their progress towards dismantling these barriers, using an original WRI methodology and data from Uber.

The purpose of this paper is to evaluate the compatibility between impact investing and the mobility sector, and identify opportunities and barriers to amplifying net-positive impacts among new mobility enterprises, illustrated through two case cities of Kampala, Uganda, and Hyderabad, India.

This working paper evaluates key considerations for the next generation of federal tax credits that can incentivize innovation and accelerate the deployment of low-carbon and clean energy technologies across the U.S. power, transportation, industrial, and buildings sectors.

This paper presents the case that progressive taxation, namely a frequent flying levy (FFL), can generate revenues for decarbonization while ensuring equitable distribution of the cost burden.

This report identifies several levers that can be activated today to catalyze the growing demand from the logistics sector and accelerate the deployment of zero-emission trucks in Europe.

Public transport is critical to addressing carbon emissions in the transport sector. The study identifies five key areas of implementation to encourage more effective NDCs: Commit to funding public transport infrastructure and operations, bus electrification, adaptation, broader sustainability measures in urban transport, and cross-sectoral and sub-national coordination.

This paper proposes a reformulation of a Mixed-Integer Programming model into a Decentralized Markov Decision Process model. It solves using a Multi-Agent Reinforcement Learning algorithm to address the scalability issues of large-scale smart grid systems.

This study investigates the potential under existing regulatory authority to expand deployment of low-carbon waste-to-electricity pathways, yielding revenue that could be used to subsidize electric vehicle (EV) sales or to support other RFS-aligned climate and transport-sector goals.

Using data covering the period from 2015 to 2020, this report explores the successful experiences from leading NEV cities in China and is intended as a guide for other similar cities, both within China and around the world.