Table of contents

The continued deployment of renewable energy is critical to the energy transition necessary for climate change mitigation. Since cost is one of the primary drivers of solar adoption, we must continue to reduce the cost of solar to hasten the transition to clean energy. Currently, solar soft costs account for 52%–70% of the cost of an installed residential solar photovoltaic (PV) system in the U.S. These costs are persistent and an increasing share of the costs, since hardware costs continue to decline. The consensus on a definition of soft costs is 'non-hardware costs', but defining something by what it is not does not tell us what it is, which makes it difficult to study and understand. To this end, we developed the solar soft cost ontology (SSCO) to provide a comprehensive view of the complex and variable knowledge domain of solar soft costs. We identify the main categories of solar soft costs, how they relate to each other, how that knowledge is created, shared, and acquired, and the relevant actors in the solar soft cost knowledge ecosystem. Developed by coding a corpus of nearly 130 academic articles, the resulting ontology in web ontology language (OWL) includes 136 classes and 87 properties. The SSCO can facilitate and enhance knowledge transfer of research findings and best practices within and between stakeholder communities, including researchers, practitioners, and policy makers, that seek to address solar soft costs. We conclude with a discussion of potential applications and use cases for the SSCO among this diverse stakeholder community.

Natural gas is a transport fuel which may help reduce greenhouse gas emissions in shipping and trucks. However, there is some disagreement regarding the potential for natural gas to provide significant improvements relative to current ships and trucks. In 2015, road freight represented ~7% of global energy related CO₂ emissions, with international shipping representing ~2.6% of global emissions. These emissions are also expected to grow, with some estimates suggesting road freight emission growing by a third, and shipping emissions growing by between 50% and 250% from 2012 to 2050, making absolute emissions reductions challenging. In addition, reducing emissions in ships and trucks has proved technically difficult given the relatively long distances that ships and trucks travel.

This paper documents a systematic review of literature detailing well-to-wheel/wake greenhouse gas emissions and economic costs in moving from diesel and heavy fuel oil to natural gas as a fuel for trucks and ships. The review found a number of important issues for greenhouse gas reduction. First, moderate greenhouse gas reductions of 10% were found when switching to natural gas from heavy fuel oil in shipping when comparing the lowest estimates. Comparing lowest well-to-wheel greenhouse gas emissions estimates for trucks, the benefit of switching to natural gas fuel is approximately a 16% reduction in greenhouse gas emissions. However, these emissions are highly variable, driven particularly by methane emissions in exhaust gas. Given this, in the worst cases natural gas ships and trucks emit more greenhouse gasses than the diesel trucks and heavy fuel oil ships that they would replace. It appears relatively cost effective to switch to natural gas as a transport fuel in ships and trucks. However, the limited emissions reduction potential raises questions for the ongoing role of natural gas to reduce greenhouse gas emissions in line with the challenging greenhouse gas reduction targets emerging in the transport sector.

Globally, about 800 million people lack access to electricity. To address this situation, the sustainable development goals (SDGs) aim to reach universal electrification by 2030, a goal that requires annual investments in the ballpark of US$50 billion over the next 10 years. Several organizations, such as the United Nations' Sustainable Energy for All initiative and the World Bank, support the governments of developing countries in their electrification efforts. Cost-minimizing models are a widely used tool to help governments decide where and in which technologies to invest or to create investment incentives for the private sector. Often, geospatial models are used to decide on technology and estimate market size for the different electrification alternatives: main grid extension, mini-grids, or solar-home systems. Here, I briefly describe the literature and identify a major weakness of these models: their ignorance of institutional quality, i.e. the quality of government, jurisdiction, regulation, and public services. I elaborate on the role of institutional quality for electrification, which strongly affects the success of projects and real-world investment decisions. I then argue that the emerging literature on off-grid financing can be used to consider institutional quality in cost-minimizing models. I conclude by recommending concrete steps that should be taken in order to make these models and thereby electrification planning and budget allocations more realistic.