Short introduction on Singapore’s emissions
Although CO2 is the driving force behind the temperature changes, other gases such as methane (CH4) also contribute their share to global warming, for example through the exploitation of gas fields, and emissions by livestock. While methane is emitted much less than CO2 on a global scale, it is a much stronger greenhouse gas (GHG). Scientists estimated the relative strength of the important Kyoto greenhouse gases so that we can convert all emissions to an equivalent of CO2 emissions. For example, the emission of one ton of methane has approximately the warming effect of 25 tons of CO2. The factor of 25 reflects the climate forcing on a 100-year time horizon, following the Global Warming Potential presented in the IPCC Fourth Assessment Report (AR4).
With greenhouse gas emissions of approximately the equivalent of 99.7 mega tonnes of CO2 (Mt CO2eq), Singapore contributed 0.20% to the global greenhouse gas emissions of 2017 (rank 56 - incl. EU27 on rank 3). All emissions estimates exclude emissions and absorption from land, which result from activities such as cutting down or planting of forests (Land Use, Land-Use Change and Forestry: LULUCF). Emissions from bunker fuels (international aviation and shipping) were also excluded, as they are not accounted for in national totals.
For 2030, Singapore’s global contribution to greenhouse gas emissions is projected to decrease to approximately 0.17% (97.1 mega tonnes of CO2 equivalent / rank 60 - incl. EU27 on rank 4). The emissions projections for Singapore were derived by downscaling the Shared Socio-Economic Pathways’ (SSPs) “Middle-of-the-Road” baseline marker scenario SSP2. These pathways describe certain narratives of socio-economic developments and were, i.a., used to derive greenhouse gas emissions scenarios that correspond to these developments. SSP2 is a narrative with little shifts in socio-economic patterns compared to historical ones, and is connected to medium socio-economic challenges for both climate mitigation and adaptation. While different models were used for each storyline, per SSP (SSPs1-5) one model was chosen as representative “marker scenario”. As the emissions projections are not readily available on country-level, but national estimates are important, the pathways were downscaled in the aftermath. In 2017, Singapore represented 0.075% of the global population. Its Gross Domestic Product (GDP) in 2017 were 0.39% of the global GDP.
Looking at the highest contributing emissions sectors and gases separately, we find that in 2017 the highest contributing emissions sectors were Energy and IPPU (88.1% and 11.3%). Amongst the greenhouse gases that are considered in the Kyoto Protocol, the strongest contributor with 96.8% was CO2. This was followed by PFCs emissions, with a significantly lower share of 1.6%. When not considering the sectors and gases independently, but the sector-gas combinations instead, Energy CO2 and IPPU CO2 (87.2% and 9.5%) represented the largest emissions in 2017.
Greenhouse gas mitigation and Nationally Determined Contribution (NDC)
In 2015, the majority of countries agreed to the Paris Agreement (PA), with the goal of “Holding the increase in the global average temperature to well below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C above pre-industrial levels, recognizing that this would significantly reduce the risks and impacts of climate change” (Article 2.1.a). Countries stated their pledges and targets towards achieving the PA’s goals in their Nationally Determined Contributions (NDCs). With Article 4.4 of the Paris Agreement, Parties decided that “Developed country Parties should continue taking the lead by undertaking economy-wide absolute emission reduction targets. Developing country Parties should continue enhancing their mitigation efforts, and are encouraged to move over time towards economy-wide emission reduction or limitation targets in the light of different national circumstances.”
In its NDC, the country communicates that “Singapore intends to peak emissions at 65 MtCO2eq [AR5] around 2030. Note: Based on current projections, this will allow us to achieve a 36% reduction in Emissions Intensity (EI) from 2005 levels by 2030.” (NDC, p. 1, GWP: p. 10). The emissions intensity reference is not mentioned in the updated 2020 NDC, but was stated to be GDP in the Singapore’s previous NDC. The availability of national estimates of emissions mitigation targets and pathways in line with countries’ NDCs is of great importance when, e.g., aggregating to global emissions to then derive, i.a., the resulting end-of-century warming levels. Singapore’s contribution is unconditional on, e.g., international financial support (NDC, p. 13). The target is meant to be “economy-wide” (NDC, p. 2), and all main IPCC sectors (Energy, IPPU, Agriculture, LULUCF, and Waste) are explicitly stated as covered (NDC, p. 3). Furthermore, all seven Kyoto GHGs are listed as covered (NDC, p. 3). In total, this results in a 100% coverage of Singapore’s NDC contribution.
The NDC-assessment is based on Singapore’s NDC submitted to the UNFCCC in March 2020.
The Figure below provides additional information, regarding both the baseline emissions used in our assessment and the quantified mitigated pathways for Singapore.
Baseline emissions and mitigated emissions pathways based on the country’s Nationally Determined Contribution. In terms of national emissions, we look at the SSP2 baseline marker scenario, and the emissions of all IPCC sectors. Contributions from LULUCF are excluded (exclLU), and the emissions are based on GWPs from AR4. The left panel (a) shows the baseline emissions, indicating the contributions of the Kyoto Greenhouse Gases CO2, CH4, N2O, and the basket of F-gases to the national emissions. If we could extract baseline data exclLU from the NDC, you can see their values as black squares (converted from GWP SAR to AR4 if needed). In the right panel (b), the quantified mitigated emissions pathways are shown, based on information from the country’s NDC and also on non-NDC emissions baselines, per target conditionality and range (marked un-/conditional best/worst). Even though not all countries have targets with different conditionalities or ranges, we need assumptions for all four cases to build one global pathway per conditionality plus range combination and to derive corresponding temperature estimates. Therefore, we indicate these four pathways here. Per combination, we performed several quantifications with differing assumptions and show the median and the minimal and maximal pathways here. Additionally, if we could quantify the targets based on data extracted purely from the NDC - or if the targets were directly given in absolute emissions, these targets are shown as squares (in the GWP originally given in the NDC).
Data sources and further information
- Historical emissions: PRIMAP-hist v2.1 (Guetschow et al., 2016, 2019).
- Historical socio-economic data: PRIMAP-hist Socio-Eco v2.1 (Guetschow et al., 2019).
- Projected emissions and socio-economic data: downscaled SSPs (Guetschow et al., 2020, 2020).
- NDC quantifications: NDCmitiQ (Guenther et al., 2020, 2021).
- GDP is given in purchasing power parity (PPP).
- Main emissions sectors (Intergovernmental Panel on Climate Change, IPCC): Energy, Industrial Processes and Product Use (IPPU), Agriculture and LULUCF (Land Use, Land-Use Change and Forestry), also named AFOLU (Agriculture, Forestry and Other Land Use), and Waste.
- Kyoto GHG: basket of several GHGs, namely carbon dioxide (CO2), Methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6), and since the second Kyoto Protocol period (2013-20) additionally nitrogen fluoride (NF3).
- Global Warming Potentials (GWPs): GHGs have very different warming potentials. To make them comparable and for aggregation purposes, GWPs are used (how much energy will 1 ton of a certain gas absorb over a defined period of time, relative to the same mass of CO2?).
1 Potsdam Institute for Climate Impact Research (PIK), 14473 Potsdam, Germany