Summary

“Report on workshops for case studies for sectoral and national innovation systems” presents a summary of the stakeholders workshops which were organized by the INNOPATHS project after the first All Partner Meeting of the project. The stakeholder workshops had two main goals. First, to establish dialogue with key representative of stakeholder groups. Second, to share with the stakeholder the draft of the framework for analysis which was developed by CMCC and UCAM as a basis for the work of Task 2.1 of the project.

Summary

The aim of the Low Carbon Pathways Platform (LCPP) is to make the newly-developed technologically-detailed decarbonisation pathways for Europe easily accessible to stakeholders from industry, policy and society. This document details the initial prototype of the two interactive visualization tools contained in the LCPP: The first tool is a user-friendly visualization interface that allows the users to access all the relevant variables developed in the project models: the scenario explorer. The second, a guided storyline exploration tool, helps users to develop an intuition both for the energy system changes required for a low-carbon transition and for the causes and effects of decarbonization actions in the different sectors and across sectors. It thus provides an entry point for users not accustomed to exploring energy system scenario data, giving them a first experience of how the energy and price quantities actually tell a story of the transformation, of the market developments and the financing needs, of the policy drivers and of the socio-economic effects.

Summary

INNOPATHS has a prominent focus on the socio-economic aspect of the low-carbon transition because arguments such as “job killing regulation” and “regressive distributional effects” are often used to undermine the political acceptability of climate policies. Identifying what may have been the distributional impacts of previous energy transitions is a key component of INNOPATHS, since it provides additional empirical support on a crucial aspect of the co-design of scenarios to capture the benefits for the winners and costs for the losers. However, winners and losers of the introduction of different policies and technologies are difficult to identify because a possible low-carbon pathway affects each actor along multiple dimensions. To address this fundamental issue, the deliverable D1.4 attempts to better understand socio-economic impacts along multiple dimensions and actors: regions, households, workers, countries, sectors and stakeholders. The diversity of approaches used to examine problems at different level of aggregation highlights the complexity of the socio-economic impacts. The report is composed of a collection of studies, each examining a particular aspect of the socio-economic impacts of past low-carbon transitions using different methodologies. We can summarize the conclusions of this deliverable into three broad insights reflecting the heterogeneity in the efforts, methodologies and stage of development of research projects presented in this report.

Insight 1: Climate policies do not appear to reduce aggregate employment, productivity and international competitiveness of EU industry and power generation sector. However, our results confirm a long-term employment decline in most carbon-intensive sectors that is likely to be explained mostly by other structural shocks, such as automation and globalization.

Insight 2 : Regarding the distributional effects, climate policies have been skill biased against manual workers and have favoured technicians and professionals. Looking at skill-biased impacts is crucial to assess the political acceptability of climate policies and the vulnerability of certain regions to the low-carbon transition.

Insight 3 : Qualitative empirical evidence reveals that energy injustice is not only an issue for centralized energy production, but also an issue for decentralized energy technologies such as solar energy and EV. Quantitative empirical evidence confirms that subsidies to low-carbon technology, i.e. energy-efficient cars in this case, can be highly regressive, and even more so for EVs (with the caveat on data quality mentioned above).