全球变化知识资源中心(globalchange): Improved representation of investment decisions in assessments of CO2 mitigation

Assessments of emissions mitigation patterns have largely ignored the huge variation in real-world factors—in particular, institutions—that affect where, how and at what costs firms deploy capital^{1, 2, 3, 4, 5}. We investigate one such factor—how national institutions affect investment risks and thus the cost of financing^{6, 7, 8}. We use an integrated assessment model (IAM; ref. 9) to represent the variation in investment risks across technologies and regions in the electricity generation sector—a pivotally important sector in most assessments of climate change mitigation¹⁰—and compute the impact on the magnitude and distribution of mitigation costs. This modified representation of investment risks has two major effects. First, achieving an emissions mitigation goal is more expensive than it would be in a world with uniform investment risks. Second, industrialized countries mitigate more, and developing countries mitigate less. Here, we introduce a new front in the research on how real-world factors influence climate mitigation. We also suggest that institutional reforms aimed at lowering investment risks could be an important element of cost-effective climate mitigation strategies.

A number of factors such as national policy environments, quality of public and private institutions, sector and technology specific risks, and firm-level characteristics can affect investors’ assessments of risks, leading to a wide variation in the business climate for investment^{6, 11}. Such heterogeneity in investment risks can have important implications, as investors usually respond to risks by requiring higher returns for riskier projects; delaying or forgoing the investments; or preferring to invest in existing, familiar technologies⁸. In this paper, we use an IAM (refs 9, 12) and incorporate decisions on investments based on risks along two dimensions (Table 1). Along the first dimension, we vary perceived risks associated with particular technologies. To do so, we assign a higher cost of capital for investment in low-carbon technologies as these involve intrinsically higher levels of regulatory and market risk (Supplementary Text, Section 1.1). The second dimension uses a proxy to vary investment risks across regions, based on an institutional quality metric published by the World Economic Forum (Fig. 1)¹¹. In addition to these two dimensions of variation in investment risks, we consider scenarios with and without a climate target. The climate policy scenarios all require a reduction in global CO₂ emissions from fossil fuels and industry of 50% in 2050 relative to 2005 levels (Supplementary Fig. 1)¹³. We restrict the analysis to investments in the electricity generation sector, which are expected to account for a significant share of future investments in the context of climate change mitigation^{2, 10}.

Table 1: Investment risk scenarios explored in this study.

Full table

This analysis uses the Global Change Assessment Model (GCAM). Outcomes of GCAM are driven by assumptions about population growth, labour participation rates and labour productivity in fourteen geo-political regions, along with representations of resources and technologies^{9, 12}. Investment in GCAM depends on relative costs and the distribution among technologies determined using a logit-choice formulation in which not all decision makers choose a technology option just because it is cheaper; higher-priced options may also get some market share^{21, 22, 23} (Supplementary Text, Section 3).

Among different variables affected by differences in investment risks, we focus on the cost of capital for investment. Risk-averse investors expect risk-adjusted rates of return, raising the cost of capital for investing in projects involving greater risk. In theory, the cost of capital affects investment at the level of the technology and the macro-economy. At the technology level, the cost of capital affects the balance between capital and running costs. On the aggregate macroeconomic level, variables such as institutional quality can affect the cost of capital, which in turn influence the rate and magnitudes of capital formation (Supplementary Text, Section 1.1). In GCAM, the cost of capital is represented in the fixed charge rate (FCR), which is the amount of revenue per dollar of capital investment that must be collected annually by an investor against carrying charges on that investment²⁴. In this analysis, we vary FCRs across technologies and regions. Whereas variation across technologies affects the choice between low-carbon technologies and fossil-fuel technologies that have capital-intensive and fuel-intensive cost structures respectively, the variation across regions is represented to capture the macroeconomic effects explained above. As a point of departure, we also consider a counterfactual uniform investment risk scenario.

To represent variation of investment risks across technologies, we compile FCR values used for financial analyses of electricity generation technologies in the United States (Supplementary Table 1). We then categorize technologies into low risk (fossil-fuel technologies) and high risk (nuclear, renewables, bioenergy, CCS) with FCRs of 13% and 17% respectively. To model non-uniformities across regions, we use country-level institutional scores from the World Economic Forum’s Global Competitiveness Index data set to calculate GDP-weighted scores for the fourteen GCAM regions. We then look at spreads of macroeconomic costs of debt and equity risk premiums across countries (Supplementary Fig. 14). Next, we represent FCRs as a log-linear function of institutional quality and adjust the parameters of the function to be consistent with the spreads observed in Supplementary Fig. 14. Not only does this representation enable us to capture the macroeconomic effects explained earlier, but also reflects behaviour of investors in the real world, where investors demand risk-adjusted rates of return.

We restrict our analysis to capital investments in electricity generation, which are expected to account for a significant share of future investment in the context of climate change mitigation². In addition, biomass-based technologies in sectors other than electricity, for example biofuels and biogas, are included to avoid our results from being influenced by availability of biomass resources. For instance, if biomass-based technologies were to be excluded, a higher risk of investing in the electricity sector would shift investment to bioenergy (which would remain low risk) to satisfy growing energy demand and meet a stringent climate target. Note that GCAM operates in a partial equilibrium framework. Conducting the analysis in a general equilibrium framework or including other key energy or land-use sectors in the analysis will provide additional insights, but will not materially affect the broad qualitative insights from our analysis.

There are several caveats to the findings of this study. First, although many paradigms to compare mitigation efforts across regions have been put forth in the literature, we consider the equal marginal abatement cost rule because it provides a baseline for comparison with many previous analyses, and also because the approach internalizes the public goods characteristics of investments in technology²⁵. The actual distribution of investments would depend on the policies and mechanisms used domestically and internationally (for example, domestic policies to encourage technology deployment, offset crediting programmes, and so on). Second, we assume that institutional qualities are constant over time. Competitive forces in the continuous interaction between institutions and organizations could drive institutional change. However, the process may be slow and path-dependent owing to economies of scale and network externalities²⁶. Finally, we do not consider mitigation from land-use changes so as to retain focus on the effects of non-uniformities in investment risks in the electricity generation sector, keeping other variables fixed^{2, 19, 27}.


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