Chapter 16: Innovation, Technology Development and Transfer
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Cross-Chapter Box 11 | Digitalisation: Efficiency Potentials and Governance Considerations
| Broader societal impacts of digitalisation can also influence climate mitigation because of induced demand for consumption goods, impacts on firms’ competitiveness, changes the demand for skills and labour, worsening of inequality – including reduced access to services due to the digital divide – and governance aspects (low evidence, medium agreement) (Sections 4.4, 5.3 and 5.6). Digital technologies expand production possibilities in sectors other than ICTs through robotics, smart manufacturing, and 3D printing, and have major implications on consumption patterns (Matthess and Kunkel 2020). Initial evidence suggests that robots displace routine jobs and certain skills, change the demand for high-skilled and low-skilled workers, and suppress wages (Acemoglu and Restrepo 2019). Digitalisation can thus reduce consumers’ liquidity and consumption (Mian et al. 2020) and contribute to global inequality, including across the gender dimension, raising fairness concerns (Kerras et al. 2020; Vassilakopoulou and Hustad 2021). Digital technologies can lead to additional concentration in economic power (e.g., Rikap 2020) and lower competition; however, open source digital technologies can counter this tendency (e.g., Rotz et al. 2019). Digital technologies play a role in mobilising citizens for climate and sustainability actions (Segerberg 2017; Westerhoff et al. 2018). |
16.5 International Technology Transfer and Cooperation for Transformative Change
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16.5.3 International Technology Transfer and Cooperation: Recent Institutional Approaches
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16.5.3.1 UNFCCC Technology and Capacity-building Institutions
Since the Kyoto Protocol’s CDM has been operational, studies have assessed its hypothesised contribution to technology transfer, including transfer of knowledge. Though not an explicit objective of the CDM, numerous papers have investigated whether CDM projects contribute to technology transfer (Michaelowa et al. 2019). The literature varies in its assessment. Some find extensive use of domestic technology and hence lower levels of international technology transfer (Doranova et al. 2010), while others indicate that around 40% of projects feature hardware or other types of international transfer of technology (Seres et al. 2009; Murphy et al. 2015), depending on the nature of technology, the host country and region (Cui et al. 2020) and the project type (Karakosta et al. 2012). The CDM was generally positively evaluated on its contribution to technology transfer. However, it was also regarded critically as the market-responsiveness and following of export implies a bias to larger, more advanced economies rather than those countries most in need of technology transfer (Gandenberger et al. 2016), although some countries have managed to correct that by directing the projects, subnationally, to provinces with the greatest need (Bayer et al. 2016). Also, the focus on hardware in evaluations of technology transfer under the CDM has been criticised (Haselip et al. 2015; Michaelowa et al. 2019). Indeed, although many studies do go beyond hardware in their evaluations (e.g., Murphy et al. 2015), the degree to which the project leads to a change in the national system of innovation or institutional capacity development is not commonly assessed, or has been assessed as limited (de Coninck and Puig 2015).
There is significantly less literature on capacity building under the UNFCCC, especially as it relates to managing the technology transition. In a legal analysis, D’Auvergne and Nummelin (2017) indicate the nature, scope and principles of Article 11 on capacity building of the Paris Agreement as being demand- and countrydriven, following a needs approach, fostering national, subnational and local ownership, and being iterative, incorporating the lessons learnt, as well as participatory, cross-cutting and gender-response. They also highlight that it is novel that least-developed countries and Small Island Developing States (SIDS) are called out as the most vulnerable and most in need of capacity building, and that it raises a ‘legal expectation’ that all parties ‘should’ cooperate to enhance the capacity in developing countries to implement the Paris Agreement. These aspects are reflected in the terms of reference of the Paris Committee on Capacity-building (PCCB) that was established in 2015 at the 21st Conference of the Parties (UNFCCC 2016; D’Auvergne and Nummelin 2017), and was extended by five years at the 25th Conference of the Parties in 2019 (UNFCCC 2020a, b). In its work plan for 2020–2024, its aims include ‘identifying capacity gaps and needs, both current and emerging, and recommending ways to address them’.
16.6 Technological Change and Sustainable Development
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16.6.1 Linking Sustainable Development and Technological Change
Sustainable development and technological change are deeply related (UNCTAD 2019). Technology has been critical for increasing productivity as the dominant driving force for economic growth. Also, the concentration of technology in few hands has boosted consumption of goods and services which are not necessarily aligned with the Sustainable Development Goals (SDGs) (Walsh et al. 2020). It has been suggested that, in order to address sustainable development challenges, science and technology actors would have to change their relation to policymakers (Ravetz and Funtowicz 1999) as well as the public (Jasanoff 2003). This has been further elaborated for the SDGs. The scale and ambition of the SDGs call for a change in development patterns that require a fundamental shift in: current best practices; guidelines for technological and investment decisions; and the wider socio-institutional systems (UNCTAD 2019; Pegels and Altenburg 2020). This is needed as not all innovation will lead to sustainable development patterns (Altenburg and Pegels 2012; Lema et al. 2015).
Current SDG implementation gaps reflect, to some extent, inadequate understanding of the complex relationships among the goals (Waiswa et al. 2019; Skene 2020), as well as their synergies and trade-offs, including how they limit the range of responses available to communities and governments, and potential injustices (Thornton and Comberti 2017). These relationships have been approached by focusing primarily on synergies and trade-offs while lacking the holistic perspective necessary to achieve all the goals (Nilsson et al. 2016; Roy et al. 2018).
A more holistic framework could envisage the SDGs as outcomes of stakeholder engagement and learning processes directed at achieving a balance between human development and environmental protection (Gibbons 1999; Jasanoff 2003), to the extent that the two can be separated. From a science, technology and innovation perspective, Fu et al. (2019) distinguish three categories of SDGs. The first category comprises those SDGs representing essential human needs for which inputs that put pressure on sustainable development would need to be minimised. These include Zero hunger (SDG 2), Clear water and sanitation (SDG 6) and Affordable and clean energy (SDG 7) resources, which continue to rely on production technologies and practices that are eroding ecosystem services, potentially hampering the realisation of SDGs 15 (Life on land) and 14 (Life below water) (Díaz et al. 2019). The second category includes those related to governance and which compete with each other for scarce resources, such as Industry, innovation and infrastructure (SDG 9) and Climate action (SDG 13), which require an interdisciplinary perspective. The third category are those that require maximum realisation, include No poverty (SDG 1), Quality education (SDG 4) and Gender equality (SDG 5) (Fu et al. 2019).
