Editing IPCC:AR6/WGIII/Chapter-16 (section)

=== 16.2.3 Directing Technological Change ===

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Technological change is characterised not only by its speed, but also its direction. The early works that considered the role of technology in economic and productivity growth ( [[#Solow--1957|Solow 1957]] ; [[#Nelson--1966|Nelson and Phelps 1966]] ) assumed that technology can move forward along only one dimension – every improvement led to an increase in efficiency and increased demand for all factors of production. This view, however, ignores the potency of technological change to alter the otherwise fixed relation between economic growth and the use of resources.

Technological change that saves fossil fuels could decouple economic growth and CO 2 emissions ( [[#Acemoglu--2012|Acemoglu et al. 2012]] , 2014; [[#Hémous--2016|Hémous 2016]] ; [[#Greaker--2018|Greaker et al. 2018]] ). Saving of fossils could be obtained with increasing efficiency of producing alternatives to fossils ( [[#Acemoglu--2012|Acemoglu et al. 2012]] , 2014). This is the case of oil consumption by combustion engine cars which could be substituted with electric cars ( [[#Aghion--2016|Aghion et al. 2016]] ). If there is no close substitute for a ‘dirty resource’, then its intensity in production could still be reduced by increasing the efficiency of the dirty resource relative to the efficiency of other inputs ( [[#Hassler--2012|Hassler et al. 2012]] ; [[#André--2014|André and Smulders 2014]] ; [[#Witajewski-Baltvilks--2017|Witajewski-Baltvilks et al. 2017]] ). For instance, energy efficiency improvement leads to a drop in relative demand for energy ( [[#Hassler--2012|Hassler et al. 2012]] ; [[#Witajewski-Baltvilks--2017|Witajewski-Baltvilks et al. 2017]] ).

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==== 16.2.3.1 Determinants of Technological Change Direction: Prices, Market Size and Government ====

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Firms change their choice of technology upon change in prices: when one input (e.g., energy) becomes relatively expensive, firms pick technologies that allow them to economise on that input, according to price-induced technological change theory ( [[#Reder--1965|Reder and Hicks 1965]] ; [[#Samuelson--1965|Samuelson 1965]] ; [[#Sue%20Wing--2006|Sue Wing 2006]] ). For example, an increase in oil price will lead to a choice of fuel-saving technologies. Such a response of technological change was evident during the oil-price shocks in the 1970s ( [[#Hassler--2012|Hassler et al. 2012]] ). Technological change that is induced by an increase in price of a resource can never lead to an increase in use of that resource. In other words, rebound effects associated with induced technological change can never offset the saving effect of that technological change ( [[#Antosiewicz--2021|Antosiewicz and Witajewski-Baltvilks 2021]] ).

The impact of energy prices on the size of low-carbon technological change is supported by large number of empirical studies ( [[#Popp--2019|Popp 2019]] ; [[#Grubb--2020|Grubb and Wieners 2020]] ). Studies document that higher energy prices are associated with a higher number of low-carbon energy or energy efficiency patents ( [[#Newell--1999|Newell et al. 1999]] ; [[#Popp--2002|Popp 2002]] ; [[#Verdolini--2011|Verdolini and Galeotti 2011]] ; [[#Noailly--2015|Noailly and Smeets 2015]] ; [[#Ley--2016|Ley et al. 2016]] ; [[#Witajewski-Baltvilks--2017|Witajewski-Baltvilks et al. 2017]] ; [[#Lin--2019|Lin and Chen 2019]] ). [[#Sue%20Wing--2008|Sue Wing (2008)]] finds that innovation induced by energy prices had a minor impact on the decline in US energy intensity in the last decades of the 20th century, and that autonomous technological change played a more important role. Several studies explore the impact of a carbon tax on green innovation ( [[#16.4|Section 16.4]] ). However, disentangling the effect of policy tools is complex because the presence of some policies could distort the functioning of other policies ( [[#Böhringer--2010|Böhringer and Rosendahl 2010]] ; [[#Fischer--2017|Fischer et al. 2017]] ) and because the impact of policies could be lagged in time ( [[#Antosiewicz--2021|Antosiewicz and Witajewski-Baltvilks 2021]] ).

The direction of technological change depends also on the market size for dirty technologies relative to the size of other markets ( [[#Acemoglu--2014|Acemoglu et al. 2014]] ). Due to this dependence, climate and trade policy choices in a single region can alter the direction of technological change at the global level ( [[#16.2.3.3|Section 16.2.3.3]] ).

The value of the market for clean technologies is determined not only by current profit, but also by a firm’s expectation of future profits ( [[#Alkemade--2012|Alkemade and Suurs 2012]] ; [[#Greaker--2018|Greaker et al. 2018]] ; [[#Aghion--2019|Aghion 2019]] ). One implication is that bolstering the credibility and durability of policies related to low-carbon technology is crucial to accelerating technological change and inducing the private sector investment required ( [[#Helm--2003|Helm et al. 2003]] ), especially in the rapidly growing economies of Asia and Africa which are on the brink of making major decisions about the type of infrastructure they build as they grow, develop, and industrialise ( [[#Nemet--2017|Nemet et al. 2017]] ).

If governments commit to climate policies, firms expect that the future size of markets for clean technologies will be large and they are eager to redirect research effort towards development of these technologies today. The commitment would also incentivise acquiring skills that could further reduce the costs of those technologies ( [[#Aghion--2019|Aghion 2019]] ). However, historical evidence shows that policies related to energy and climate over the long term have tended to change ( [[#Taylor--2012|Taylor 2012]] ; [[#Nemet--2013|Nemet et al. 2013]] ; [[#Koch--2016|Koch et al. 2016]] ). Still, where enhancing policy durability has proven infeasible, multiple uncorrelated potentially overlapping policies can provide sufficient incentives ( [[#Nemet--2010|Nemet 2010]] ).

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==== 16.2.3.2 Determinants of Direction of Technological Change: Financial Markets ====

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The challenges of investing in innovation in energy when compared to other important areas, such as ICT and medicine are also reflected in the trends in venture capital funding. Research found that early-stage investments in cleantech companies were more likely to fail and returned less [https://www.sciencedirect.com/topics/social-sciences/capital capital] than comparable investments in software and medical technology ( [[#Gaddy--2017|Gaddy et al. 2017]] ). This led to investors retreating from hardware technologies required for renewable energy generation and storage, and moving to software-based technologies and demand-side solutions ( [[#Bumpus--2017|Bumpus and Comello 2017]] ).

The preference for particular types of investments in renewable energy technologies depends on investors attitude to risk ( [[#Mazzucato--2018|Mazzucato and Semieniuk 2018]] ). Some investors invest in only one technology, others may spread their investments, or invest predominantly in high-risk technologies. The distribution of different types of investors will affect whether finance goes to support deployment of new high-risk technologies, or diffusion of more mature, less-risky technologies characterised by incremental innovations. The role of finance in directing investment is further discussed in Chapter 15, [[IPCC:Wg3:Chapter:Chapter-15#15.6.2|Section 15.6.2]] .

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==== 16.2.3.3 Internationalisation of Green Technological Change ====

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A unilateral effort to reduce emissions (via a combination of climate, industrial and trade policies) in a coalition of regions that are technology leaders will reduce the cost of clean technologies, and induce emissions reduction in the countries outside the coalition ( [[#Golombek--2004|Golombek and Hoel 2004]] ; [[#Di%20Maria--2005|Di Maria and Smulders 2005]] ; [[#Di%20Maria--2008|Di Maria and van der Werf 2008]] ; [[#Hémous--2016|Hémous 2016]] ; [[#van%20den%20Bijgaart--2017|van den Bijgaart 2017]] ). The literature suggests various mechanisms leading to this result. [[#Di%20Maria--2008|Di Maria and van der Werf (2008)]] argue that the effort to reduce emissions in one region reduces global demand for ‘dirty goods’. This will redirect global innovation towards clean technologies, leading to a drop in the cost of clean production in every region.

The model in Hemous (2016) predicts that such a coalition could induce acceleration of clean technological change through a mix of carbon taxation, clean R&amp;D subsidies and trade policies in that region leading to reduction of cost of clean production inside the coalition. Export of goods produced with clean technologies to a region outside the coalition reduces demand for dirty goods in that region. In the model by [[#van%20den%20Bijgaart--2017|van den Bijgaart (2017)]] local advancements of clean technologies by a coalition with strong R&amp;D potential are imitated outside the coalition. Furthermore, advancements of clean technologies will incentivise future clean R&amp;D outside the coalition due to intertemporal knowledge spillovers. In [[#Golombek--2004|Golombek and Hoel (2004)]] an increase in environmental concern in one region increases abatement R&amp;D in that region. Part of this knowledge spills over to other regions, increasing their incentive to increase abatement too, provided that the latter regions did not invest in abatement before.

However, this chain breaks if the regions that are behind the technological frontier (i.e., technological followers) are not able to absorb the solutions developed by regions at the frontier. New technologies might fail due to deficiencies of political, commercial, industrial, and financial institutions, which we list in Table 16.4. For instance, countries might not benefit fully from international knowledge spillovers due to insufficient domestic R&amp;D investment, since local knowledge is needed to determine the appropriateness of technologies for the local market, adapting them, installing and using effectively ( [[#Gruebler--2012|Gruebler et al. 2012]] ). From the policy perspective, this implies that simple transfer of technologies could be insufficient to guarantee adoption of new technologies ( [[#Gruebler--2012|Gruebler et al. 2012]] ).

'''Table 16.4 | Examples of institutional deficiencies preventing deployment of new technologies in countries behind the technolo''' '''gical frontier.'''

{| class="wikitable"
|-
! Institutions

! Examples of deficiencies

! Literature reference

|-
| Industrial

| Inability to benefit fully from international knowledge spillover due to insufficient domestic R&amp;D investment

| [[#Mancusi--2008|Mancusi (2008)]] ; [[#Unel--2008|Unel (2008)]] ; [[#Gruebler--2012|Gruebler et al. (2012)]]

|-
| Commercial

| Insufficient experience with the organisation and management of large-scale enterprise

| [[#Abramovitz--1986|Abramovitz (1986)]] ; [[#Aghion--2005|Aghion et al. (2005)]]

|-
| Political

| Vested interests and customary relations among firms and between employers and employees

| [[#Olson--1982|Olson (1982)]] ; [[#Abramovitz--1986|Abramovitz (1986)]]

|-
| Financial

| Financial markets incapable of mobilising capital for individual firms at large scale

| [[#Abramovitz--1986|Abramovitz (1986)]] ; [[#Aghion--2005|Aghion et al. (2005)]]

|}

Research relying on patent citations has indicated that Foreign Direct Investment (FDI) is a mechanism for firms to contribute to the recipient country’s innovation output as well as benefit from the recipient country in industrialised countries ( [[#Branstetter--2006|Branstetter 2006]] ) and in developing countries ( [[#Newman--2015|Newman et al. 2015]] ). However, insights specific for energy or climate change mitigation areas are not available, nor is there much information about how other innovation metrics may react to FDI.

Finally, technologies could be not efficient in developing countries, even if they are efficient in countries at the technological frontier. For instance, technologies that are highly capital intensive and labour saving will be efficient only in countries where costs of capital are low and costs of labour are high. Similarly, technologies which require a large number of skilled labour will be more competitive in a country where skilled labour is abundant (and hence cheap) than where it is scarce ( [[#Basu--1998|Basu and Weil 1998]] ; [[#Caselli--2006|Caselli and Coleman 2006]] ).

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==== 16.2.3.4 Market Failures in Directing Technological Change ====

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Market forces alone cannot deliver Pareto optimal (i.e., social) efficiency due to at least two types of externalities: GHG emissions that cause climate damage; and knowledge spillovers that benefit firms other than the inventor. [[#Nordhaus--2011|Nordhaus (2011)]] argues that these two problems would have to be tackled separately: once the favourable intellectual property right regimes (i.e., the laws or rules or regulation on protection and enforcement) are in place, a price on carbon that corrects the emission externality is sufficient to induce optimal level of green technological change. [[#Acemoglu--2012|Acemoglu et al. (2012)]] demonstrates that subsidising clean technologies (and not dirty ones) is also necessary to break the lock-in of dirty technological change. Recommendations for technical changes are often based on climate considerations only and neglect secondary externalities and environmental costs of technology choices (such as loss of biodiversity due to inappropriate scale-up of bioenergy use). The scale of adverse side effects and co-benefits varies considerably between low-carbon technologies in the energy sector ( [[#Luderer--2019|Luderer et al. 2019]] ).

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