A unified modelling framework for projecting sectoral greenhouse gas emissions

Using a simple conceptual framework and time series econometric methods, we project greenhouse gas emissions under a business-as-usual narrative for 173 countries and five main sectors until 2050, adding to the toolkit available to assess the progress towards a carbon-free future.
A unified modelling framework for projecting sectoral greenhouse gas emissions

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Limiting global warming to an acceptable degree requires swift and decisive action at all scales to reduce greenhouse gas (GHG) emissions in a sustainable and efficient manner. Specific policies to do so are often designed, decided, and implemented on a national level and target particular sectors. However, comprehensive and comparable projections of GHG emissions on a sectoral level covering a global sample of countries are missing. With our study, we aim to fill this gap by providing projections of GHG emissions up until 2050 for 173 countries and five main sectors: energy systems, industry, transportation, agriculture, and buildings. The results of our analysis provides governments with a valuable source of information that should support evidence-based climate policy at the national level.  

Characterising and modelling sectoral GHG emissions

We start from a simple conceptual framework that characterises sectoral emissions as the product of their main drivers: emission intensities (as proxy for technological and policy advancements), GDP per capita (as a measure of affluence) and overall population. We augment our model with additional demographic variables that have been shown to be important determinants of sectoral emissions in the literature: the age and education structure of an economy, as well as its degree of urbanization.  We then use an econometric approach inspired by the macroeconomic time series modelling literature that jointly models the interdependencies between these variables. The nature of our proposed method efficiently pools information across countries to address model complexity, while simultaneously allowing for country/sector heterogeneity where the data deems it necessary.

Based on the estimated model parameters and trajectories of the socioeconomic indicators given by the Shared Socioeconomic Pathways (SSPs), we derive sectoral emission projections in the next step. These projections are representative for a scenario in which both technological advancements and climate policies roughly follow past trends, thus depicting a “business-as-usual” (BAU) scenario. Our estimation framework accounts for uncertainty regarding the interactions between variables and provides a lower bound of the overall uncertainty regarding future sectoral emissions under these assumptions.

Country details

This extensive projection exercise results in more than 850 trajectories of GHG emissions on a country/sector level. Figure 1 illustrates the projections for three of the largest contributors to global emissions: emissions in the Chinese energy-producing sector, Indian industrial emissions, and US transport emissions. Whereas emission intensities in all these sectors have a strong falling trend (panel a), the resulting sectoral emissions show differing developments (panel b). The projected decreases in the intensity of the Chinese energy-producing sector, together with slower population and output growth under SSP2, lead to a peak of the median forecast in the corresponding emissions in 2036. In contrast, as is the case for many other emerging and developing countries, the decrease in emission intensities of the Indian industrial sector is outweighed by strong population and GDP increases, leading to stark emissions growth under the BAU scenario. For the US transport sector, our projections show a reduction in GHG emissions, however at a slow pace. Such slow progress in the decarbonisation of transportation is also projected for many other advanced countries, where emissions from this sector are either still increasing or decreasing with the slowest pace across sectors.

We believe that such detailed country-sector results can provide important insights for policymakers, researchers, and the interested public. To facilitate their exploration, our data are featured on the World Emissions Clock (https://worldemissions.io/), which has been developed in cooperation with the World Data Lab (https://worlddata.io/). The World Emissions Clock is an interactive data visualization tool that includes comparisons to emission trajectories for other scenarios different from BAU.

Illustrative country-sector level projections showing (a) sectoral emission intensities and (b) sectoral emissions for the Chinese energy-producing, the Indian industry, and the US transport sector. Black line denotes historical values until 2018 and the median forecast thereafter. Red shaded areas denote the 68% and 90% predictive posterior intervals.
Figure1: Illustrative country-sector level projections showing (a) sectoral emission intensities and (b) sectoral emissions for the Chinese energy-producing, the Indian industry, and the US transport sector. Black line denotes historical values until 2018 and the median forecast thereafter. Red shaded areas denote the 68% and 90% predictive posterior intervals. 

Regional and global results

In addition to making the projections comparable across countries and sectors, the unifying nature of our conceptual and statistical framework allows for a straightforward aggregation of results to a regional level in a bottom-up manner. Figure 2 shows that our projections imply important changes in the geographical and sectoral distribution of GHG emissions in the coming decades. While energy production is still the major source of emissions, accounting for roughly 40% of overall emissions, it is the transport sector that exhibits the largest relative increases, almost doubling from 7.2GT CO2eq in 2018 to 13.8GT CO2eq in 2050. Some notable sectoral shifts at the regional level include the relative decline of emissions from industry in Eastern Asia (mostly driven by China) at the expense of strongly rising transport emissions or the displacement of agriculture by industry as the second-largest contributing sector in South Asia. The largest increase in total GHG emissions is projected for Southern and South-Eastern Asia as well as Africa, although their per capita emissions remain below the global average, causing 37% of total emissions while containing 57% of the world’s population. The Middle East is the only region where our projections indicate stagnating emission intensities under BAU, resulting in a strong increase in aggregate emissions and making it the region with the highest per capita emissions in 2050.

Figure 2: Regional overview of aggregate GHG emissions in 2018 and 2050 for IPCC regions

Future perspectives

The modelling tool developed in the study is an important addition to the toolkit available to researchers and policymakers to assess the progress and identify potential impediments towards a carbon-free future. As such, there are many potentially fruitful avenues for future research building on our data and methods. One promising application is to compare them to BAU trajectories produced by national environmental agencies, e.g. as part of their Nationally Determined Contributions (NDCs). As Figure 3 suggests, some countries that formulate goals in their NDCs against such a benchmark can have an incentive to inflate their BAU estimates in order to make reduction pledges less binding. Having a set of comparable projections available for a large sample of countries are an important ingredient to conduct such exercises at scale, which can be crucial to assess the progress in the fight against climate change. 

Figure 3: Comparison of emission trajectories to national BAU scenarios (GHG emission levels scaled to historical values as reported in Minx et al. (2021) for the base year used in the respective country's NDC). Black line denotes historical values until 2018 and the median projection thereafter. Red shaded areas denote the 68% and 90% posterior predictive intervals. Blue line denotes national BAU scenario, green line the pledged reduction under the unconditional NDC.

Similarly, our projections can be used to assess reference scenarios derived from Integrated Assessment Models at the global, regional or national level. A vital extension of our study concerns the coherent integration of emissions resulting from land use (change) under a similar set of assumptions, for which some hurdles regarding their measurement would have to be addressed. Last but not least, the combination of our data with recently proposed approaches to identify the causal effect of particular policies for the reduction of GHG emissions on the sectoral level holds the promise to present policymakers with a portfolio of suitable tools for effective climate policy.

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Climate Change
Physical Sciences > Earth and Environmental Sciences > Earth Sciences > Climate Sciences > Climate Change
Time Series Analysis
Mathematics and Computing > Statistics > Applied Statistics > Statistics in Business, Management, Economics, Finance, Insurance > Econometrics > Time Series Analysis
Resource and Environmental Economics
Humanities and Social Sciences > Economics > Resource and Environmental Economics

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