Editing IPCC:AR6/WGIII/TS (section)

=== TS.6.5 Innovation, Technology Development and Transfer ===

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'''Innovation in climate mitigation technologies has seen enormous activity and significant progress in recent years. Innovation has also led to, and exacerbated, trade-offs in relation to sustainable development.''' Innovation can leverage action to mitigate climate change by reinforcing other interventions. In conjunction with other enabling conditions, innovation can support system transitions to limit warming and help shift development pathways. The currently widespread implementation of solar PV and LED lighting, for instance, could not have happened without technological innovation ''.'' Technological innovation can also bring about new and improved ways of delivering services that are essential to human well-being ( ''high confidence'' ) {16.1, 16.3, 16.4, 16.6} . At the same time as delivering benefits, innovation can result in trade-offs that undermine both progress on mitigation and progress towards other Sustainable Development Goals (SDGs). Trade-offs include negative externalities’ – for instance, greater environmental pollution and social inequalities – rebound effects leading to lower net emission reductions or even increases in emissions, and increased dependency on foreign knowledge and providers ( ''high confidence'' ). Effective governance and policy have the potential to avoid and minimise such misalignments ( ''medium evidence'' , ''high agreement'' ). {16.2, 16.3, 16.4, 16.5.1, 16.6}

'''A systemic view of innovation to direct and organise the processes has grown over the last decade. This systemic view of innovation takes into account the role of actors, institutions, and their interactions, and can inform how innovation systems that vary across technologies, sectors and countries, can be strengthened (''' '''''high confidence''''' ''') {16.2, 16.3, 16.5} .''' Where a systemic view of innovation has been taken, it has enabled the development and implementation of indicators that are better able to provide insights in innovation processes. This, in turn, has enabled the analysis and strengthening of innovation systems. Traditional quantitative innovation indicators mainly include R&amp;D investments and patents. Figure TS.26 illustrates that energy-related research, development and demonstration (RD&amp;D) has risen slowly in the last two decades, and that there has been a reorientation of the portfolio of funded energy technologies. Systemic indicators of innovation, however, go well beyond these approaches. They include structural innovation system elements including actors and networks, as well as indicators for how innovation systems function, such as access to finance, employment in relevant sectors, and lobbying activities {16.3.4, Table 16.7} . For example, in Latin America, monitoring systemic innovation indicators for the effectiveness of agroecological mitigation approaches has provided insights on the appropriateness and social alignment of new technologies and practices {Box 16.5} . Climate-energy-economy models, including integrated assessment models (IAMs), generally employ a stylised and necessarily incomplete view of innovation, and have yet to incorporate a systemic representation of innovation systems. {16.2.4, Box 16.1}

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'''Figure TS.26 |''' '''Fraction of public energy research, development and demonstration (RD&amp;D) spending by technology over time for IEA (largely OECD) countries between 1974 and 2018.''' {Box 16.3, Figure 1}

'''A systemic perspective on technological change can provide insights to policymakers supporting their selection of effective innovation policy instruments (''' '''''high confidence''''' ''') {16.4, 16.5} .''' A combination of scaled-up innovation investments with demand-pull interventions can achieve faster technology unit cost reductions and more rapid scale-up than either approach in isolation ''.'' These innovation policy instruments would nonetheless have to be tailored to local development priorities, to the specific context of different countries, and to the technology being supported. The timing of interventions and any trade-offs with sustainable development also need to be addressed. Public R&amp;D funding and support, as well as innovation procurement, have shown to be valuable for fostering innovation in small-to-medium clean-tech firms (Figure TS.27) {16.4.4.3} . Innovation outcomes of policy instruments not necessarily aimed at innovation, such as feed-in tariffs, auctions, emissions trading schemes, taxes and renewable portfolio standards, vary from negligible to positive for climate change mitigation. Some specific designs of environmental taxation can also result in negative distributional outcomes {16.4.4} . Most of the available literature and evidence on innovation systems come from industrialised countries and larger developing countries. However, there is a growing body of evidence from developing countries and Small Island Developing States (SIDS). {16.4, 16.5, 16.7}

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'''Figure TS.27''' '''|''' '''Technology innovation process and the (illustrative) roles of different public policy instruments (on the right-hand side).''' {Figure 16.1} Note that demand-pull instruments in the regulatory instrument category, for instance, can also shape the early stages of the innovation process. Their position in the latter stages is highlighted in this figure because typically these instruments have been introduced in latter stages of the development of the technology. {16.4.4}

'''Experience and analyses show that technological change is inhibited if technological innovation system functions are not adequately fulfilled; this inhibition occurs more often in developing countries (''' '''''high confidence''''' ''').''' Examples of such functions are knowledge development, resource mobilisation, and activities that shape the needs, requirements and expectations of actors within the innovation system (guidance of the search). Capabilities play a key role in these functions, the buildup of which can be enhanced by domestic measures, but also by international cooperation. For instance, innovation cooperation on wind energy has contributed to the accelerated global spread of this technology. As another example, the policy guidance by the Indian government, which also promoted development of data, testing capabilities and knowledge within the private sector, has been a key determinant of the success of an energy-efficiency programme for air conditioners and refrigerators in India. {16.3, 16.5, 16.6, Cross-Chapter Box 12 in Chapter 16, Box 16.3}

'''Consistent with innovation system approaches, the sharing of knowledge and experiences between developed and developing countries can contribute to addressing global climate and the SDGs. The effectiveness of such international cooperation arrangements, however, depends on the way they are developed and implemented (''' '''''high confidence''''' ''').''' The effectiveness and sustainable development benefits of technology sharing under market conditions appears to be determined primarily by the complexity of technologies, local capabilities and the policy regime. This suggests that the development of planning and innovation capabilities remains necessary, especially in Least-Developed Countries (LDCs) and SIDS. International diffusion of low-emission technologies is also facilitated by knowledge spillovers from regions engaged in clean R&amp;D ( ''medium confidence'' ). {16.2}

'''The evidence on the role of intellectual property rights (IPR) in innovation is mixed. Some literature suggests that it is a barrier while other sources suggests that it is an enabler to the diffusion of climate-related technologies (''' '''''medium confidence''''' ''').''' There is agreement that countries with well-developed institutional capacity may benefit from a strengthened IPR regime, but that countries with limited capabilities might face greater barriers to innovation as a consequence. This enhances the continued need for capacity-building. Ideas to improve the alignment of the global IPR regime and addressing climate change include specific arrangements for LDCs, case-by-case decision-making and patent-pooling institutions. {16.2.3, 16.5, Box 16.10}

'''Although some initiatives''' '''have mobilised investments in developing countries, gaps in innovation cooperation remain, including in the Paris Agreement instruments. These gaps could be filled by enhancing financial support for international technology cooperation, by strengthening cooperative approaches, and by helping build suitable capacity in developing countries across all technological innovation system functions (''' '''''high confidence''''' ''').''' The implementation of current arrangements of international cooperation for technology development and transfer, as well as capacity-building, are insufficient to meet climate objectives and contribute to sustainable development. For example, despite building a large market for mitigation technologies in developing countries, the lack of a systemic perspective in the implementation of the Clean Development Mechanism (CDM), operational since the mid-2000s, has only led to some technology transfer, especially to larger developing countries, but limited capacity building and minimal technology development ( ''medium confidence'' ). In the current climate regime, a more systemic approach to innovation cooperation could be introduced by linking technology institutions, such as the Technology Mechanism, and financial actors, such as the Financial Mechanism. {16.5.3}

'''Countries are exposed to sustainable development challenges in parallel with the challenges that relate to climate change. Addressing both sets of challenges simultaneously presents multiple and recurrent obstacles that systemic approaches to technological change could help resolve, provided they are well managed (''' '''''high confidence''''' ''').''' Obstacles include both entrenched power relations dominated by vested interests that control and benefit from existing technologies, and governance structures that continue to reproduce unsustainable patterns of production and consumption ( ''medium confidence'' ). Studies also highlight the potential of cultural factors to strongly influence the pace and direction of technological change. Sustainable solutions require adoption and mainstreaming of locally novel technologies that can meet local needs, and simultaneously address the SDGs. Acknowledging the systemic nature of technological innovation – which involve many levels of actors, stages of innovation and scales – can lead to new opportunities to shift development pathways towards sustainability. {16.4, 16.5, 16.6}

'''Strategies for climate change mitigation can be most effective in accelerating transformative change when actions taken to strengthen one set of enabling conditions also reinforce and strengthen the effectiveness of other enabling conditions (''' '''''medium confidence''''' ''').''' Applying transition or system dynamics to decisions can help policymakers take advantage of such high-leverage intervention points, address the specific characteristics of technological stages, and respond to societal dynamics. Inspiration can be drawn from the global unit-cost reductions of solar PV, which were accelerated by a combination of factors interacting in a mutually reinforcing way across a limited group of countries ( ''high confidence'' ) {Box 16.2, Cross-Chapter Box 12 in Chapter 16} . Transitions can be accelerated by policies appropriately targeted, which may be grouped in different ‘pillars of policy’. The relative importance of different ‘pillars’ differs according to the stage of the transition. (Figure TS.28) {1.2.3}

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'''Figure TS.''' '''28 |''' '''Transition dynamics: levels, policies and processes.''' {Figure 1.7} The relative importance of different ‘pillars of policy’ differs according to the stage of the transition. The lower panel illustrates growth of innovative technologies or practices, which if successful, emerge from niches into an S-shaped dynamic of exponential growth. The diffusion stage often involves new infrastructure and reconfiguration of existing market and regulatory structures. During the phase of more widespread diffusion, growth levels off to linear, then slows as the industry and market matures. The processes displace incumbent technologies/practices which decline, initially slowly, but then at an accelerating pace. Many related literatures identify three main levels with different characteristics, most generally termed ''micro, meso'' and ''macro'' .

'''Better and more comprehensive data on innovation indicators can provide timely insights for policymakers and policy design locally, nationally and internationally, especially for developing countries, where such insights are often missing.''' Data needed include those that can show the strength of technological, sectoral and national innovation systems. It is also necessary to validate current results and generate insights from theoretical frameworks and empirical studies for developing countries’ contexts. Innovation studies on adaptation and mitigation other than energy and ''ex-pos'' ''t'' assessments of the effectiveness of various innovation-related policies and interventions, including R&amp;D, would also provide benefits. Furthermore, methodological developments to improve the ability of IAMs to capture energy innovation system dynamics and the relevant institutions and policies (including design and implementation), would allow for more realistic assessment. {16.2, 16.3, 16.7}

'''Box TS.14 | Digitalisation'''

Digital technologies can promote large increases in energy efficiency through coordination and an economic shift to services, but they can also greatly increase energy demand because of the energy used in digital devices ( ''high confidence'' ). {Cross-Chapter Box 11 in Chapter 16, 16.2}

Digital devices, including servers, increase pressure on the environment due to the demand for rare metals and end-of-life disposal. The absence of adequate governance in many countries can lead to harsh working conditions and unregulated disposal of electronic waste. Digitalisation also affects firms’ competitiveness, the demand for skills, and the distribution of, and access to resources. The existing digital divide, especially in developing countries, and the lack of appropriate governance of the digital revolution can hamper the role that digitalisation could play in supporting the achievement of stringent mitigation targets. At present, the understanding of both the direct and indirect impacts of digitalisation on energy use, carbon emissions and potential mitigation is limited ( ''medium confidence'' ).

The digital transformation is a megatrend that is fundamentally changing all economies and societies, albeit in very different ways depending on the level of development of a given country and on the nature of its economic system. Digital technologies have significant potential to contribute to decarbonisation due to their ability to increase energy and material efficiency, make transport and building systems less wasteful, and improve the access to services for consumers and citizens. Yet, if left unmanaged, the digital transformation will probably increase energy demand, exacerbate inequities and the concentration of power, leaving developing economies with less access to digital technologies behind, raise ethical issues, reduce labour demand and compromise citizens’ welfare. Appropriate governance of the digital transformation can ensure that digitalisation works as an enabler, rather than as a barrier and further strain in decarbonisation pathways. Governance can ensure that digitalisation not only reduces GHG emissions intensity but also contributes to reducing absolute GHG emission, constraining run-away consumption. {Cross-Chapter Box 11 in Chapter 16, 16.2}

Digital technologies have the potential to reduce energy demand in all end-use sectors through steep improvements in energy efficiency. This includes material input savings and increased coordination as they allow the use of fewer inputs to perform a given task. Smart appliances and energy management, supported by choice architectures, economic incentives and social norms, effectively reduce energy demand and associated GHG emissions by 5–10% while maintaining equal service levels. Data centres can also play a role in energy-system management, for example by waste-heat utilisation where district heat systems are close by; temporal and spatial scheduling of electricity demand can provide about 6% of the total potential demand response. {5.5, Cross-Chapter Box 11, Table 1 in Chapter 16}

Digital technologies, analytics and connectivity consume large amounts of energy, implying higher direct energy demand and related carbon emissions. Global energy demand from digital appliances reached 7.14 EJ in 2018. The demand for computing services increased by 550% between 2010 and 2018 and is now estimated at 1% of global electricity consumption. Due to efficiency improvements, the associated energy demand increased only modestly, by about 6% from 2000 to 2018. {Box 9.5}

System-wide effects endanger energy and GHG-emission savings. Rising demand can diminish energy savings, and also produce run-away effects associated with additional consumption and GHG emissions if left unregulated. Savings are varied in smart and shared mobility systems, as ride-hailing increases GHG emissions due to deadheading, whereas shared pooled mobility and shared cycling reduce GHG emissions, as occupancy levels and/or weight per person kilometre transported improve. Systemic effects have wider boundaries of analysis and are more difficult to quantify and investigate but are nonetheless very relevant. Systemic effects tend to have negative impacts, but policies and adequate infrastructures and choice architectures can help manage and contain these. {5.3, 5.4, 5.6}

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