A look at the displacement effects of going green
Sustainability efforts may end up passing the buck to another point along the supply chain, resulting in a heavier carbon footprint there. In their study, Fan et al. investigate how retired coal-fired plants in China shifted the burden of production to existing plants.
Wong Wei Chen
30 August 2023
Electric vehicles (EVs) are fast gaining traction, and are apparently making inroads into the prestige market also. In Forbes Wheels’ “Best Luxury Electric Cars For 2023”, published early this year, luxury marques like Porsche, BMW and Mercedes Benz made the list, as did Tesla, the brand that is synonymous with EVs.
Equipped with lithium-ion batteries, EVs are powered by clean energy – and are much vaunted as the future of motoring. Yet, MIT Climate Portal estimates that for every tonne of mined lithium, 15 tonnes of CO2 are emitted into the air. The true environmental impact of EVs is therefore “displaced” by carbon emission elsewhere along the supply chain, and any accurate assessment would need to take this displacement effect into account.
Similar displacement effects likewise happen in other sectors, and in their 2022 study, “The unintended consequences of coal fired power plant closures: evidence from China”, Fan, Gao and Tang, investigate how closed plants shifted the burden of production to other existing plants to meet the high demand for energy.
Background
In a bid to reduce air pollution and achieve carbon neutrality by 2060, the Chinese government in 2000 started to phase out coal-fired power plants over the course of 15 years between 2001 and 2015.
However, clean-energy infrastructure was still nascent and inadequate to replace the closed plants, with the result that coal plants that were still open had to operate longer hours to make up for the energy shortfall.
Empirical design
Using the Global Energy Monitor website, Fan et al. collected information pertaining to more than 1,700 power plants across China, which included 367 retired plants. This information was then cross-referenced with high-resolution satellite data measuring monthly sulphur dioxide (SO2) levels from 2000 to 2014, which allowed the researchers to assess the impact of plant closures on SO2 levels over time.
The treatment group comprised power grids within 35km of a retired plant, while the control group covered those between 35km – 50km of retired plants. The difference in air pollution between these two groups measures the impact of plant closure on air quality.
To assess the displacement effect on still-operating plants having to make up for the energy shortfall created by closed plants, the researchers identified operational plants within 100km of retired plants and examined the air pollution of grids within their vicinity.
Findings
With controls in place for socioeconomic factors, climate characteristics and various fixed effects, base regression analysis found that the treatment group on average experienced a 2.8% drop in SO2 levels compared to the control group.
To address possible endogeneity and strengthen the identification of plant closure (and therefore cessation of coal combustion) as exerting a causal effect on air quality, the researchers conducted a series of robustness checks.
As the combustion of coal does not release ozone (O3), Fan et al. conducted a “placebo test” using O3 as the outcome variable in their regression model. Results showed no significant change in O3 emissions after the coal-fired plants were retired.
As another robustness test, the researchers expanded the locus of the control group from the original 35km – 50km to a wider 35km – 75km. The coefficient of the treatment term of the regression model was likewise negative, and in fact increased to -3.3% from the baseline -2.8%.
A wider geographical spread for the control group meant that grids in this category were, on average, further away from the treatment group (and power plant), thereby suggesting that the average air pollution for the bigger control group would be even lower than that of the original control group. That in turn suggested a bigger differential between treatment and control groups. The larger treatment effect of -3.3% corroborates this assumption and furnishes further evidence that plant closure has had a causal effect on air quality.
Other tests included the removal of outlying data, clustering of data at different geographical levels (e.g. county vs provincial) and collapsing monthly data into a yearly format. Results were consistent with those found in the baseline analysis.
Displacement effects
Plant closures would cause nearby plants that were still operating to increase operating hours. Given that China’s clean-energy facilities were still inadequate during the period under study, existing plants would have to “work harder” to compensate for the energy shortfall.
Regression analysis found that treatment grids (< 35km) in the vicinity of operating plants within a 100km radius of retired plants experienced a 1.5% increase in SO2 emissions, which netted off the 2.8% reduction for treatment grids near closed plants.
Robustness tests again included the clustering of data at different geographical levels (city, county, grid) and regression results were likewise positive and statistically significant. Treatment effects were in fact more pronounced compared to the overall sample average of 1.5%, ranging between 2% – 3%, since clustering data at more granular levels is likely to sieve out the endogenous effects of unobserved variables.
Policy implications
In view of findings uncovered by the study, retired plants and their impact on the immediate neighbourhoods cannot be assessed in isolation, since those closures had apparently resulted in the redistribution of pollution, rather than overall reduction.
At the broader level, this entails that any assessment of environmental impact ought to take the entire supply chain into account, and while Scope 1 and Scope 2 emissions are often within the control of an organisation, Scope 3 emissions generated further up and down the chain are also an important issue that needs to be tackled for effective and long-lasting sustainability.
Yi Fan is an assistant professor in the Department of Real Estate, NUS Business School, National University of Singapore.
Qiuxia Gao is a PhD student at the NUS Business School, National University of Singapore.
Cheng Keat Tang is an Assistant Professor in Economics, Nanyang Technological University.