Technical Change in Photovoltaics and the Applicability of the Learning Curve Model

The extent and timing of cost-reducing improvements in low-carbon energy systems are important sources of uncertainty in the future levels of greenhouse-gas emissions. Models that assess the costs of climate change mitigation policy rely heavily on learning curves to incorporate changes in technology. Historically, no energy technology has changed more dramatically than photovoltaics (PV), the cost of which has declined by a factor of nearly 100 since the 1950s. Which changes were more important in accounting for the cost reductions that have occurred over the past three decades? Are these results consistent with the widely held learning-by-doing theory of technological change? We gather empirical data and populate a simple model to identify the most important factors affecting the cost of PV. We find that the learning curve theory only weakly explains change in the most important factors- plant size, module efficiency, and the cost of silicon

Chapter 24: Policies for the Energy Technology Innovation System (ETIS)

Innovation and technological change are integral to the energy system transformations described in the Global Energy Assessment (GEA) pathways. Energy technology innovations range from incremental improvements to radical breakthroughs and from technologies and infrastructure to social institutions and individual behaviors. This Executive Summary synthesizes the main policy-relevant findings of Chapter 24 . The innovation process involves many stages – from research through to incubation, demonstration, (niche) market creation, and ultimately, widespread diffusion. Feedbacks between these stages influence progress and likely success, yet innovation outcomes are unavoidably uncertain. Innovations do not happen in isolation; interdependence and complexity are the rule under an increasingly globalized innovation system. Any emphasis on particular technologies or parts of the energy system, or technology policy that emphasizes only particular innovation stages or processes (e.g., an exclusive focus on energy supply from renewables, or an exclusive focus on Research and Development [R&D], or feed-in tariffs) is inadequate given the magnitude and multitude of challenges represented by the GEA objectives. A first, even if incomplete, assessment of the entire global resource mobilization (investments) in both energy supply and demand-side technologies and across different innovation stages suggests current annual Research, Development & Demonstration (RD&D) investments of some US$50 billion, market formation investments (which rely on directed public policy support) of some US$150 billion, and an estimated US$1 trillion to US$5 trillion investments in mature energy supply and end-use technologies (technology diffusion). Major developing economies like Brazil, India and above all China, have become significant players in global energy technology RD&D, with public- and private-sector investments approaching US20 billion, or almost half of global innovation investments, which is significantly above the Organisation for Economic Co-operation and Development (OECD) countries’ public-sector energy RD&D investments (US13 billion). Important data and information gaps exist for all stages of the energy technology innovation investments outside public sector R&D funding in OECD countries, particularly in the areas of recent technology-specific private sector and non-OECD R&D expenditures, and energy end-use diffusion investments. Analysis of investment flows into different stages of the innovation process reveals an apparent mismatch of resource allocation and resource needs. Early in the innovation process, public expenditure on R&D is heavily weighted toward large-scale supply-side technologies. Of an estimated US$50 billion in annual investment globally, less than US$10 billion are allocated to energy end-use technologies and energy efficiency. Later in the innovation process, annual market (diffusion) investment in supply-side plant and infrastructure total roughly US 2005 $0.8 trillion, compared with a conservative estimate of some US$1–4 trillion spent on demand-side technologies. These relative proportions are, however, insufficiently reflected in market deployment investment incentives of technologies, which almost exclusively focus on supply-side options, to the detriment of energy end use in general and energy efficiency in particular foregoing also important employment and economic growth stimuli effects from end-use investments that are critical in improving energy efficiency. The need for investment to support the widespread diffusion of efficient end-use technologies is also clearly shown in the GEA pathway analyses. The demand side generally tends to contribute more than the supply-side options to realizing the GEA goals. This apparent mismatch suggests the necessity of rebalancing public innovation expenditure and policy incentives to include smaller-scale demand-side technologies within innovation portfolios . Given persistent barriers to the adoption of energy-efficient technologies even when they are cost competitive on a life cycle basis, technology policies need to move toward a more integrated approach, simultaneously stimulating the development as well as the adoption of energy efficiency technologies and measures. R&D initiatives that fail to incentivize consumers to adopt the outcomes of innovation efforts (e.g., promoting energy-efficient building designs without strengthened building codes, or Carbon Capture and Storage [CCS] development without a price on carbon) risk not only being ineffective but also precluding the market feedback and learning that are critical for continued improvements in technologies. Little systematic data are available for private-sector innovation inputs (including investments), particularly in developing countries. Information is patchy on innovation spillovers or transfers between technologies, between sectors, and between countries. It is also not clearly understood how fast knowledge generated by innovation investments may depreciate, although policy and investment volatility are recognized as critical factors. Technical performance and economic characteristics for technologies in the lab, in testing, and in the field are not routinely available. Innovation successes are more widely documented than innovation failures. Although some of the data constraints reflect legitimate concerns to protect intellectual property, most do not. Standardized mechanisms to collect, compile, and make data on energy technology innovation publicly available are urgently needed. The benefits of coupling these information needs to public policy support have been clearly demonstrated. A positive policy example is provided by the early US Solar Thermal Electricity Program, which required formal, non-proprietary documentation of cost improvements resulting from public R&D support for the technology. The energy technology innovation system is founded on knowledge generation and flows. These are increasingly global, but this global knowledge needs to be adapted, modified, and applied to local conditions. The generation of knowledge requires independent and stable institutions to balance the competing needs and interests of the market, policy makers, and the R&D community. The technology roadmaps and the policy regime that characterize innovation in end-use technologies in the Japanese Top Runner program are a good example of the actor coordination and knowledge exchange needed to stimulate technological innovation. Generated knowledge needs to spread through the innovation system. Knowledge flows and feedbacks create and strengthen links between different actors. This can take place formally or informally. Policies that are overly focused on the development of technological “hardware” should be rebalanced to support interactions and learning between actors. The provision of test facilities in the early years of the Danish wind industry is a good example of how policy can support knowledge flows and the strengthening of collaborative links within networks of actors in an innovation system (energy companies, turbine manufacturers, local owners). Long-term, consistent, and credible institutions underpin investments in knowledge generation, particularly from the private sector, and consistency does not preclude learning. Knowledge institutions must be responsive to experience and adaptive to changing conditions. Although knowledge flows through international cooperation and experience sharing cannot presently be analyzed in detail, the scale of the innovation challenge emphasizes their importance alongside efforts to develop the capacity to absorb and adapt knowledge to local needs and conditions. The current global cooperation in energy technology innovation is well illustrated by the International Energy Agency (IEA) technology cooperation programs reviewed in Section 4.4 ; all invariably show a sparse involvement from developing countries. Clear, stable, and consistent expectations about the direction and shape of the innovation system are necessary for innovation actors to commit time, money, and effort with only the uncertain promise of distant returns. To date, policy support for the innovation system has been characterized by volatility, changes in emphasis, and a lack of clarity. The debilitating consequences on innovation outcomes of stop-go policies are well illustrated by the wind and solar water heater programs in the United States through the 1980s, as well as the large-scale (but fickle) US efforts to develop alternative liquid fuels (Synfuels). The legacy of such innovation policy failures can be long lasting. The creation of a viable and successful Brazilian ethanol industry through consistent policy support over several decades, including agricultural R&D, guaranteed ethanol purchase prices, and fuel distribution infrastructures, as well as vehicle manufacturing (flex fuel cars), is a good example of a stable, aligned, and systemic technology policy framework. It is worth noting that even in this highly successful policy example, it has taken some three decades for domestic renewable ethanol to become directly cost competitive with imported gasoline. Policies need also to be aligned . Innovation support through early research and development is undermined by an absence of support for their demonstration to potential investors and their subsequent deployment in potential markets. Policies to support innovations in low-carbon technologies are undermined by subsidies to support carbon-intensive technologies. Fuel efficiency standards that set minimum (static) efficiency floors fail to stimulate continuous technological advances, meaning innovations in efficiency stagnate once standards are reached. As a further example of misalignment, the lack of effective policies to limit the demand for mobility mean efficiency improvements can be swamped by rising activity levels. Policies should support a wide range of technologies. However seductive they seem, “silver bullets” do not exist without the benefit of hindsight. Innovation policies should use a portfolio approach under a risk-hedging and “insurance policy” decision-making paradigm. Portfolios need to recognize also that innovation is inherently risky. Failures vastly outnumber successes. Experimentation, often for prolonged periods (decades rather than years), is critical to generate the applied knowledge necessary to support the scaling up of innovations to the mass market. The whole energy system should be represented in innovation portfolios, not only particular groups or types of technologies; the entire suite of innovation processes should be included, not just particular stages or individual mechanisms. Less capital-intensive, smaller-scale (i.e., granular ) technologies or projects are less of a drain on scarce resources, and failure has less serious consequences. Granular projects and technologies with smaller scales (MW rather than GW) therefore should figure prominently in any innovation portfolio. Finally, public technology policy should not be beholden to incumbent interests that favor support for particular technologies that either perpetuate the lock-in of currently dominant technologies or transfer all high innovation risks of novel concepts to the public sector

Emissions Trends and Drivers (Chapter 2)

Global net anthropogenic greenhouse gas (GHG) emissions during the last decade (2010–2019) were higher than at any previous time in human history (high confidence). Since 2010, GHG emissions have continued to grow, reaching 59 ± 6.6 GtCO2-eq in 2019,1 but the average annual growth in the last decade (1.3%, 2010–2019) was lower than in the previous decade (2.1%, 2000–2009) (high confidence). Average annual GHG emissions were 56 ± 6.0 GtCO2-eq yr –1 for the decade 2010–2019 growing by about 9.1 GtCO2-eq yr –1 from the previous decade (2000–2009) – the highest decadal average on record (high confidence)

Cities Transformation

The International Institute for Applied Systems Analysis (IIASA), in collaboration with its Japan National Member Organization (NMO), supported by Ministry of the Environment Japan (MOEJ) has initiated a joint research endeavor centered on Cities Transformation. The primary aim of this collaborative effort's inaugural phase is the compilation of a fast-track comprehensive report. This report aims to consolidate the current understanding of urban transformations, identifying critical knowledge gaps therein. It serves to support the scientific community, city stakeholders, and policy-makers in discerning areas warranting greater attention and, crucially, further investigation and study. Particularly, it provides updates on the IPCC 7th Assessment Cycle, with a specific focus on its Special Report on Climate Change and Cities. Commencing with an overview of global urbanization trends, the report delves into the status of informal urban settlement development (Section 2). Subsequently, it comprehensively discusses the physical aspects of climate change, including impacts, vulnerability, adaptation, and mitigation, with a spotlight on air pollution and waste challenges (Section 3). Section 4 shifts focus to systems transformation, examining sectors and cross-cutting perspectives through various case studies. Following this, Section 5 investigates the enabling conditions for city transformations, emphasizing the roles of governance, behavioral and lifestyle changes, innovation and technology, and finance. Expanding further, Section 6 assesses the synergies and trade-offs of mitigation and adaptation strategies, with a particular lens on Sustainable Development Goals (SDGs), health, and well-being. The report concludes by recognizing significant knowledge gaps highlighted through expert insights

The State of Carbon Dioxide Removal - 1st Edition

A global, independent scientific assessment of Carbon Dioxide Remova