Working toward decarbonization
My research interest is in the economic effects of climate change, in particular attempts to decarbonize industries, and how the various climate policy instruments can be used effectively. Currently I am working on the modeling of industry in energy-economic and integrated assessment models, to get a deeper understanding of workable energy transition pathways and effective industrial decarbonization towards carbon neutrality.
In late 2020, Prime Minister Suga pledged that Japan will achieve net carbon neutrality by 2050, as part of a target to limit the increase in global temperature to less than two degrees in this century. This is the most ambitious climate goal ever, and added pressure to efforts at industrial decarbonization.
Achieving this goal will require a more detailed and workable understanding of energy transition pathways. Can the industrial sector be a little late in the 2050 agenda? How expensive will the deployment of negative emission technologies in industries be? What are the impacts of mitigation options on the supply-side and demand-side?
To confront this problem effectively, we need to understand it fully, and for this we need a way to assess and compare energy end-use technologies in the industrial sector in existing integrated assessment models in Asia. My work in WIAS aims to establish a platform for assessing and comparing, and unify technical description parameters to a standard template and build a database of parameters for energy end-use technologies including those with low readiness levels. I’m hoping this kind of “soft-linking” may reduce inter-model uncertainties, which is crucial to inform the climate policy debate and provide important insights for the industrial decarbonization in Japan and the rest of Asia.
Energy-intensive industries, such as steel and cement production, are extremely difficult to decarbonize due to their demand level and huge government subsidies, the time taken to update energy infrastructure, the existence of process emissions (besides those from fuel combustion), and the need for high temperatures. This is particularly true for emerging economies undergoing rapid industrialization and urbanization, such as China, India and Brazil. Japan has an additional issue — heavy industry is the most carbon-emitting sector (unlike other G7 countries, for example, where usually transport is the biggest emitting sector as shown in the figure). Also, the energy efficiency of the industry in Japan is very high compared to most other countries, which makes it more difficult to decarbonize.
Modeling of the industry
In partial energy system models, the optimization goals are to minimize the system cost in a specific region for all sectors, subject to the constraints of resources, energy use technologies, commodity balances and emissions, as well as social factors like labor supply. The results of emission pathways from Integrated Assessment Models have been used as important references in IPCC reports for a long time. New challenges also come to modelers: given the current context — the most ambitious emission reduction goal ever — how detailed is detailed enough for integrated assessment models? What are the key indicators serving the policymaking processes?
Furthermore, regarding the modeling of the industry sector, we need to involve perspectives other than the partial equilibrium in the energy system itself. Some critical issues can be:
- industry as an intermediate user and supplier to final material/energy service demands;
- industry as a globally interlinked network including the processes of material/energy recycling;
- the dynamics of industry end-use technology development;
- the dynamics of how the industry sector will benefit from a “greener” energy supply and how it will support such supply-side transition toward carbon neutrality.
Importance of data platform and tools
Synthesizing and communicating knowledge on climate change to policymakers and stakeholders is often difficult due to the complexity and diversity of research. We are also working on a better way to “translate” research results into visible and readable key indicator illustrations. The open-source R package mipplot is one attempt. We hope, as next steps, we can offer more flexibility to both experts and non-experts, making “translating” and sharing data much easier.
Long term research interests: feasibility of carbon neutrality
More and more people are getting curious about the feasibility of carbon neutrality. Just like integrated assessment models give binary results of feasibility (i.e., solvable scenarios are feasible), some hard constraints determine the assessment of system feasibility in the binary role. In the contrast, the rest constraints “make outcomes comparatively less feasible” as they have a “probability of success conditional upon trying” (Brennan & Southwood, 2007; Gilabert & Lawford-Smith, 2012). They may also affect outcomes in a non-economic mechanism.
We will focus on these constraints, define them as soft constraints or more specifically probabilistic constraints. The clarification of probabilistic constraints contributes to the assessment of possible outcomes in the future (ex-ante analysis), rather than the assessment of performances in the past (ex-post analysis).
Interview and composition: Robert Cameron
In cooperation with: Waseda University Graduate School of Political Science J-School