Environmental Research Letters LETTER • OPEN ACCESS Recent citations Normalisation of Paris agreement NDCs to - Taking stock of national climate policies to enhance transparency and ambition evaluate implementation of the Paris Agreement Mark Roelfsema et al To cite this article: Lewis C King and Jeroen C J M van den Bergh 2019 Environ. Res. Lett. 14 - A multi-level climate club with national and 084008 sub-national members: theory and application to US states Nick Martin and Jeroen C J M van den Bergh View the article online for updates and enhancements. This content was downloaded from IP address 92.57.32.56 on 17/06/2020 at 14:00 Environ. Res. Lett. 14 (2019) 084008 https://doi.org/10.1088/1748-9326/ab1146 LETTER Normalisation of Paris agreement NDCs to enhance transparency OPEN ACCESS and ambition RECEIVED 22 November 2018 Lewis C King1 and Jeroen C J M van den Bergh1,2,3 REVISED 1 24 February 2019 Institute of Environmental Science and Technology, Universitat Autònoma de Barcelona, Spain 2 ICREA, Barcelona, Spain ACCEPTED FOR PUBLICATION 3 School of Business and Economics & Institute for Environmental Studies, VU University Amsterdam, The Netherlands 20 March 2019 PUBLISHED E-mail: LewisCarl.King@uab.cat 26 July 2019 Keywords: climate change policy, climate change mitigation, Paris agreement Original content from this work may be used under the terms of the Creative Abstract Commons Attribution 3.0 licence. The Paris Agreement takes a bottom-up approach to tackling climate change with parties submitting Any further distribution of pledges in the form of nationally determined contributions (NDCs). Studies show that the sum of this work must maintain attribution to the these national pledges falls short of meeting the agreement’s 2 °C target. To explore this discrepancy, author(s) and the title of the work, journal citation we analyse individual pledges and classify them into four categories. By doing so, a lack of consistency and DOI. and transparency is highlighted, which we correct for by performing a normalisation that makes pledges directly comparable. This involves calculating changes in emissions by 2030, using data for the most recent base year of 2015. We find that pledges framed in terms of absolute emission reductions against historical base years generally produce the greatest ambition, with average emission reductions of 16% by 2030. Pledges defined as GDP intensity targets perform the worst with average emission increases of 61% by 2030. We propose that a normalisation procedure of the type as we develop becomes part of the NDC process. It will allow to not only increase the transparency of pledges for policymakers and wider society, but also promote more effective NDCs upon revision as is foreseen to happen every 5 years under the ‘ratcheting mechanism’ of the agreement. 1. Introduction much complexity and variance exists among NDCs. It remains challenging to compare what pledges really The Paris Agreement (UNFCCC 2015) takes the form mean in emission and temperature terms. This job has of a bottom-up approach to tackling climate change, instead fallen on scientists, requiring them to spend reliant on voluntary commitments in the form of valuable resources analysing the impact of the pledges nationally determined contributions (NDCs), which and judging their ambition (Aldy et al 2016, Jacoby are publicly accessible through the NDC registry. The et al 2017). NDCs are subject to regular review and updated every Initial studies suggest current NDCs are insuffi- five years, implying a system of ‘pledge and review’ cient at meeting the top-down goal (Höhne et al 2017), with civil society holding countries to account instead implying a warming of 2.6 °C–3.1 °C, dependent on of employing direct enforcement mechanisms speculative negative emission technology (Anderson (Jacquet and Jamieson 2016). Under such a process, and Peters 2016, Schleussner et al 2016, Rogelj et al however, transparency and comparability of indivi- 2016a). Here we evaluate this discrepancy by perform- dual country pledges become paramount to the ing a bottom-up assessment of the transparency and agreement’s success. In response, online tools, such as ambition of individual pledges. We identify four main the NDC Explorer (Pauw et al 2016), Climate Watch categories of NDCs, and analyse the ambition of each (Climate Watch 2018), the Climate Equity Reference category of NDC. In this context, we define ambition Calculator (Kemp-Benedict et al 2017) and the Cli- as producing emission targets that limit emission mate Action Tracker (Climate Action Tracker 2018) growth or lead to emission reductions in line with the have appeared to make pledges more comparable. Yet overall goal of limiting global temperature rise to 2 °C. the main lesson from using such tools is merely how To compare the ambition of each pledge, we perform a © 2019 The Author(s). Published by IOP Publishing Ltd Environ. Res. Lett. 14 (2019) 084008 normalisation that makes their differences compar- to make up a significant proportion of total national able and quantifies their effect on global emissions. emissions. The importance of this is illustrated for This analysis is performed by grouping countries China, which only includes CO2 in its target. This according to geographic region and by ranking them means 2.5 GtCO2e, or 4.9% of global greenhouse gas in terms of emission intensity per capita. On the basis (GHG) emissions, is excluded from their commit- of the findings we detect potential counterproductive ment. Similarly, countries can choose which IPCC systemic effects and suggest how transparency and reporting sectors fall within the NDC, with perhaps ambition levels of NDCs can be improved. the most important exclusion often being land use, The Paris Agreement states that the principles of land use change and forestry (LULUCF). Only around transparency, accuracy, completeness, comparability half of all countries fully and explicitly include and consistency (UNFCCC 2015) should be adhered LULUCF in their NDC or INDC submission. Hence, a to in accounting for emissions. We argue that these further 1% of global emissions are not covered, and principles, particularly those of transparency and this conceivably gives license for LULUCF emissions comparability need to be extended to the framing of to grow in these countries. NDCs themselves. Recent cooperation experiments have shown the need for greater transparency in 2.2. Normalisation of NDCs achieving a fair climate deal (Hurlstone et al 2017). More fundamentally, large differences exist among This will have the benefit of making the pledges easier how countries express their individual targets. Based to interpret and scrutinise by external stakeholders, on analysing all NDCs (see section 2) we group them including civil society, alongside having the psycholo- into four main categories: gical effect of inducing a behavioural change to increase ambition (Thaler and Sunstein 2008). 2.2.1. Absolute emission reduction targets Countries submitting these pledges present absolute 2. Methods emission reductions for a target year in percentage terms relative to a historic base year. The base year is set 2.1. NDC differences regarding scope and gases by the country and ranges from 1990 to 2014, while the covered target year is typically 2030, and in a few cases 2025. At the time of this writing, 147 parties had submitted their first NDC (including a joint submission for all 28 2.2.2. ‘Business as usual’ (BAU) reduction (covering also EU member states), the vast majority of which are trajectory targets) unchanged from their intentions (Intended NDC or Countries submitting these pledges present a percent- INDC) at the time the Paris Agreement was signed. A age reduction in emissions relative to a ‘business as further 18 countries submitted an INDC but are yet to usual’ scenario, typically to 2030. This scenario is formalise an NDC. From here on NDCs is used to defined by each country itself, causing a large variance denote the combination of NDCs and INDCs, also as to exist in emissions growth among scenarios. Also one may safely assume that for countries without an included in this category are the few countries NDC yet it is likely that their INDC submission is (Bhutan, Ethiopia, Oman and South Africa—repre- representative for their future NDC submission. senting only around 1.5% of global emissions) which Some large distinctions among the NDCs are submitted a fixed emission trajectory target. This immediately clear, such as whether targets are desig- approach effectively produces the same result. nated as ‘conditional’ or ‘unconditional’. Around 80% of parties submitted conditional targets, which are subject to various stipulations, such as access to inter- 2.2.3. Emission intensity reductions national finance, technology transfer and interna- Countries submitting these pledges present a reduc- tional cooperation. These are sometimes explicit, but tion in emission intensity per GDP relative to a historic often implicit or somewhat vague in their specific base year. Emissions targets are therefore dependent requirements. Meeting these conditions provides an upon the historic GDP and emissions in the base year, additional challenge, but even if they are all met, a and any future GDP growth. recent study indicates that emissions are unlikely to be on a substantially better trajectory for staying within 2.2.4. Projects absent of GHG-emission targets the 2 °C target (Rogelj et al 2016a). This final category includes countries presenting Several other significant differences between NDCs that did not include an explicit GHG emission NDCs relate to emission scope and coverage. As coun- target. These submissions typically offered details tries can decide which Kyoto protocol gases are cov- about projects aiming to reduce emissions, such as ered by the NDC, often we see them excluding HFCs, investment in renewable energy. However, these are PFCs, SF6 and NF3, which is justified by their insignif- difficult to convert into actual impact on emissions icant contribution to national emissions. However, and do not provide a hard limit on emissions for the some countries also exclude CH4 and N2O, which tend countries to be held accountable against. 2 Environ. Res. Lett. 14 (2019) 084008 Figure 1. Historical and projected GHG emissions by region. Projected 2030 GHG emissions are shown for two scenarios: only unconditional pledges are achieved; and all conditional pledges are achieved. These therefore indicate the range of effects from current commitments. In the case of India (part of South Asia), we assume the emission intensity of GDP remains unchanged from 2015 if their conditional pledges are not met. To analyse the ambition of each pledge type, we Category iv), growth was forecast from trends between group countries by pledge type and perform analyses 2006 and 2015, again using an exponential smoothing by region and rankings of emission intensity per method. Finally, the Bahamas and Belize show large capita. This allows for systematic comparison of coun- changes in global emissions between 2006 and 2015 in tries of similar geopolitical characteristics, each cover- the PRIMAP database. For these two countries, fore- ing at least 5% of global GHG emissions. The casts were made from trends between 2011 and 2015. geographical groupings avoid having to report results Furthermore, in cases of the NDC pledge holding until for relatively small nations and thus assist in present- 2025, such as for the United States, the trend extra- ing the results in a concise way that allows for clear polated to 2030 by applying exponential smoothing on interpretations. Appendix A shows the resulting the data from 2016 to 2025. Appendix B shows the groups and countries within each. The PRIMAP data- groupings of countries by emissions per capita in 2015, base (Gütschow et al 2018) was used for historical used to produce the calculations reported in table 2. GHG emissions because it is a peer-reviewed data Countries were ranked based on the absolute emis- source that covers all countries which have submitted sions of all GHGs, and then divided into five groups an NDC. Countries’ GHG emission projections to based on this ranking. For the production of all tables 2030 were calculated by individually assessing their and figures, excluding figure 1, we assume that all NDC submissions. Where LULUCF or non-CO2 requirements of conditional pledges have been ful- GHG were not covered in the pledge, such emissions filled, thus we are working with conditional pledges are forecasted using exponential smoothing methods rather than unconditional pledges. applied to trends between 2006 and 2015. Of the seven countries submitting category iii targets, only India presented an explicit GDP projection in their NDC. 3. Results Few long-term GDP projections provide complete information available at the country level. Among Table 1 shows the percentage of 2015 global emissions these, the most reliable are those provided by the for the four groups. It also mentions weighted percent- OECD (2018), which we use to calculate the expected age changes of projected emissions by 2030 compared reduction in emissions. This involves applying world to 2015. Absolute values of the emissions in 2015 and average growth rates for countries without individual 2030 are included in appendix C. In effect, we are GDP projections. In the case of India, we calculate an normalising the emission pledges for all countries, by additional emission projection for 2030, based on the converting them into a format similar to category i GDP projection that was included in its NDC. When pledges, but indicating actual emission change, growth in a country’s GHG emissions could not be whether positive or negative, compared with a con- interpreted from the NDC (i.e. countries falling into sistent base year. Levels of ambition are more easily 3 Environ. Res. Lett. 14 (2019) 084008 Table 1. Normalised (I)NDCs by pledge type and region. Deeper green shades represent greater emission reductions and deeper red colours greater emission growth. For definitions of regions and details of calculations, see section 2. a The 386% change in LAC intensity reduction countries is so high due to Chile having large negative emissions from LULUCF in the 2015 data, giving a very low overall emission total. This produces a very high percentage increase which is not representative of the change in emissions exclusive of LULUCF. Excluding Chile, this figure would only include the emission growth of Uruguay, which equals 104%. The 219% increase refers to that for India. In 2005, the base year for their intensity target, LULUCF emissions were much higher than in 2015. As with Chile, this large percentage increase seems largely related to LULUCF in the data used. judged on the basis of such normalised pledges than is due to category iv pledges being based on recent for pledges in their original form. trends; the more intense emitters are likely to have The category i pledges generally prove to have the achieved high growth in emissions, whilst low-inten- highest ambition in terms of tangible emission reduc- sity countries are likely to have not. Overall, emission tions. By contrast, categories ii–iv tend to produce low growth is lower under category iv pledges than under ii ambitions with significant emission increases of and iii. However, as the category iv pledges have been 29%–61% at global level. The effect of these changes calculated by projecting recent trends in emissions, it on emissions at the regional level is illustrated in is foreseeable that emission growth could accelerate figure 1. Notably, we find that Northern America and and be higher than that seen in categories ii and iii EU+ are the only regions aiming for absolute reduc- countries, given that there is no fixed target which they tions in emissions, while substantial increases are are pledging to limit their emission growth to. expected in MENA and South Asia. Since countries in In terms of global emissions, we see a rise of the Global North (corresponding to the Annex I coun- 23.8%, from 49.8 GtCO2 in 2015 to 61.6 GtCO2 in tries in the Kyoto Protocol) are more likely to submit 2030. Rogelj et al (2016b) calculate for a >66% chance category i pledges, it may not seem surprising that they of staying within 2 °C of warming, the remaining car- produce the targets with highest ambition. bon budget has a range of 590–1240 GtCO2. The To control for the fact that these countries tend to cumulative emissions to 2030 from our analysis are have higher emission intensity and thus are more 892 GtCO2, suggesting that the 2 °C budget will quite likely to submit category 1 pledges, we present results likely have already been spent by 2030. It should be with countries grouped by their 2015 emission per noted however that our emission projections are capita in table 2. We see that category i pledges still around 10% higher than Rogelj et al (2016a) and tend to outperform the other pledge categories within UNEP (2016). This is largely due to us trying to present the same emission intensity grouping. Additionally, it the NDC pledges ‘as is’ rather than running them indicates that for the lowest 20% of countries in terms through complex modelling without making assump- of emissions intensity pledge, type iv performs better tions of how they translate into policies. Here we are in terms of emissions reduction than types i (−8 versus presenting rather a maximum estimation of what the 64%) and ii (63%), while for the highest 20% coun- countries are actually committing to in emission tries, pledge type iii comes out lower than i (−49 ver- terms. The effect on overall emissions, and why it may sus −20%). Although this seems counter-intuitive, it differ to other publications is also likely due to the 4 Environ. Res. Lett. 14 (2019) 084008 Table 2. Normalised (I)NDCs by pledge type and emission intensity per capita. Deeper green shades represent greater emission reductions and deeper red colours represent greater emission growth. For definitions of ranking groups and details of calculations, see section 2. a The 2864% change corresponds to the percentage change of Chile, the only country in the category. in intensity reduction is so high due to Chile having large negative emissions from LULUCF in the 2015 data, giving a very low overall emission total. This produces a very high percentage increase which is not representative of the change in emissions exclusive of LULUCF. The 219% increase refers to that for India. In 2005, the base year for their intensity target, LULUCF emissions were much higher than in 2015. As with Chile, this large percentage increase seems largely related to LULUCF in the data used. sensitivity to the PRIMAP emission dataset and GDP countries much freedom to define their business-as- assumptions used. This is particularly seen in the very usual scenarios. As a result of the scenarios being self- high growth rate in emissions for India. defined, the resulting differences amongst the emis- sion changes for each countries are pronounced. At first sight, Pakistan’s conditional BAU reduction of 4. Discussion 20% may appear roughly comparable to Mexico’s 22% unconditional GHG target. However, if we normalise We now explore the variance in transparency and both relative to 2015 emissions, we see a striking dif- ambition among different pledge types. Figure 2 ference between how they translate into 2030 emis- illustrates emission projections for five sample coun- sions. Mexico’s GHG emissions decrease 11% from tries varying in ambition: Australia, India, Mexico, Pakistan and Russia. These countries were chosen as 2015 levels, while Pakistan’s pledge would increase they provide good examples of the difficulty in emissions by 182%. Despite producing only half of comparing level of ambition amongst individual Mexico’s emissions in 2015, Pakistan may emit twice countries. Australia and Russia both submitted see- as much as Mexico by 2030. The key difference is in the mingly similar category i pledges. Although we this detail of the BAU scenarios; Pakistan’s BAU scenario appears to be the most effective pledge category, due to entails a 241% increase from 2015-level emissions the explicit fixed cap on future emissions, we still find while Mexico’s is a more modest 38%. much variance in ambition, suggesting a need for Although few countries opted for category iii pled- achieving more consistency in the future. The choice ges, they include the major emitters China and India, of base year is particularly relevant. For instance, which together account for around a third of global Russia aims to reduce emissions by 25% relative to the emissions. The outcome of these targets is more diffi- base year 1990, which at face value seems comparable cult to predict. Their greatest weakness is that they do to Australia’s 26%–28% reduction relative to 2005. not limit emissions: the more growth, the higher the However, comparing both to 2015 emissions, we see emissions. These targets also suffer from needing eco- Australia’s emissions drop by around 9%, whereas nomic data for interpretation. Using current long- Russia’s increase by 13%, as it had considerably higher term GDP growth forecasts from the OECD emission levels back in 1990. It is therefore clear that (OECD 2018) India’s 33% reduction in intensity will each country’s level of ambition cannot necessarily be likely translate to emissions growth of 229% from inferred from their NDC, even when submitting 2015 levels. India include a GDP projection for 2030 in category i pledges. their NDC, namely a 173% increase from their 2014 Likewise, a similar issue occurs when trying to GDP. The OECD projections are very similar, namely compare category ii ‘BAU reduction’ pledges. Mexico a 178% increase in GDP, resulting in a comparable and Pakistan submitted Category ii, which give result in terms of CO2e emissions with a 213% 5 Environ. Res. Lett. 14 (2019) 084008 Figure 2. Historical and projected emissions for referenced countries under NDC. Dashed lines indicate projections based on current NDCs for: Australia (Category i pledge, 26% reduction from 2005 base year), Russia (Category i pledge, 25% reduction from 1990 base year); Mexico (Category ii pledge, conditional 36% BAU GHG reduction pledge); Pakistan (Category ii pledge, conditional 20% BAU reduction); and India (Category iii pledge, conditional 33% intensity reduction). Figure 3. Normalised conditional NDC pledges for all countries. The size of each sphere represents total greenhouse gas emissions for each country in 2015. The top 15 emitters and some of the more significant outliers are explicitly mentioned (the centre of a country name coincides with the centre of the respective sphere). Extreme outliers in terms of conditional emission growth and emissions per capita, and those with negative emissions have been excluded from the graph to avoid that discernibility would be compromised. These are Bhutan, Burundi, Chile, Gabon, Fiji, Trinidad and Tobago and Qatar. increase from 2015. This small range is shown in further illustrates the difficulties in clearly assessing figure 2. However, it is important to note that the countries’ pledges under the current framework. Note percentage increases are very high partly due to the that it is not our intention with these illustrations to accounting for LULUCF in the data. There was a large question goals agreed for developing countries, but to drop in LULUCF emissions between 2005 and 2006, illustrate that in their current form even a simple com- the result of which can be seen clearly in figure 2. This parison of NDCs requires careful analysis. Detailed is particularly significant given that 2015 was the cho- analysis of mitigation effort or fairness will require sen base year for India’s intensity target. However, this more informative metrics (Aldy et al 2017). 6 Environ. Res. Lett. 14 (2019) 084008 Due to normalisation of all NDCs we are able to reduce their NDC or to increase it less in the future compare these now systematically. This results in than originally intended. figure 3, which shows the data of emission growth and The current format of pledges presents two pro- emissions per capita for all individual countries, blems. Firstly, it is difficult to accurately assess and excluding a few outliers. While there is a large degree compare what the pledges will mean in actual emission of variance among countries, category 1 targets gen- terms. Russia, India and Pakistan all frame their NDCs erally lead to more ambitious emission targets, regard- in terms of percentage reductions; Russia relative to a less of emissions per capita. The full data on base year, India relative to emissions per GDP and normalised pledges for each individual country is pre- Pakistan relative to a BAU scenario. Not only does this sented in appendix D. The majority of countries fall make the associated pledges difficult to interpret and into an area between −25% and 75% emission compare to other pledges without detailed analysis, growth, and up to 10 TCO2e emissions per capita. but may produce a psychological effect of reducing However, three groups of outliers exist. Firstly, there ambition level. Psychology shows us that the framing are countries with very high emission growth; most of information is important in the decision-making significantly Pakistan, India, Iran and Turkey. These process (Thaler and Sunstein 2008). Russia, for countries have much greater growth in emissions instance, might have been more ambitious in its compared to countries of similar emission intensity. pledge if it was unable to frame its actual 13% increase Secondly, there are category 1 countries with already in emissions as a 25% decrease against a base year of very high emission intensities, including Russia, USA, choice. The same applies for all pledges in categories Canada and Australia. These countries are generally 1–3: here pledges are being framed as percentage aiming for relatively significant emission reductions reductions, although the actual effect of the reduction with the exception of Russia. Thirdly, there are several rarely corresponds to the actual effect on emissions. In high-emission intensity countries, such as the United view of this, our advice would be to prevent countries Arab Emirates and Saudi Arabia, which can be expec- from presenting their pledges in a frame which ted to continue to significantly grow their emissions appears more ambitious than its true effect. This is in due to having submitted category iv targets. line with experiments demonstrating how aversion The calculations underlying the tables and figures from shame is a powerful motivator in public contrib- assume that nations stick to, and successfully meet ution (Samek and Sheremeta 2014). This would likely their stated pledge. There is a lot of potential for coun- be challenging to achieve politically, as one of the fac- tries to meet or exceed their targets, particularly if eco- tors that brought countries into the agreement was the nomic growth turns out to be below current freedom to be able to able to set their own targets in a projections. However, one may expect the targets to be format of their choosing. However, what we are pro- difficult to achieve due to systemic effects, such as car- posing is not a radical change. For the majority of bon rebound (Druckman et al 2011, Antal and van den countries (those not in category iv), this would involve Bergh 2014), carbon leakage (Babiker 2005) and the a simple conversion based on already available data, as green paradox (Sinn 2012). Of these, carbon leakage is we have done so in this analysis, so would not prove to of particular interest to our discussion as it is influ- be an undue burden on parties. Instead, it will be enced by differences in national policy stringency, important achieving the goal of the Paris Agreement which can be connected to differences in NDCs. Car- through the ratcheting mechanism. Alternatively, the bon leakage arises when companies move production countries could submit in a flexible format but their ‘ratcheted pledges’ would be immediately normalised to countries with weaker climate policies or when by the UNFCCC. If known before, this would create imports from such countries increase due to products healthy pressure on delivering ambitious updates. being cheaper than ones from other countries or pro- duced domestically. This merely transfers emissions from one country to another. As we have seen, coun- 5. Conclusion tries fall into four tiers of pledge ambition, which cre- ates the problem of carbon leakage from countries The Paris Climate Agreement was undoubtedly a giant with relatively ambitious NDCs to those with relatively step in the right direction for international climate non-ambitious NDCs. Although category iv NDCs policy. However, studies have shown that, in its theoretically lead to less emission growth than cate- current form, it is at best inadequate and at worst gory ii and iii pledges in our analysis, one should grossly ineffective. Civil society has the right to be able expect considerable carbon leakage to these countries to clearly understand and compare climate change as they lack a target for emissions. This will make com- commitments by countries, including whether they pliance of the latter countries with their NDCs more are fair, ambitious and add up to international climate difficult, leading to higher cost of compliance—as goals. Moreover, providing consistent and easily more emissions have to be reduced than foreseen— comparable information about national climate which in turn may lead to efforts by such countries to goals has been found to contribute to their public 7 Environ. Res. Lett. 14 (2019) 084008 acceptance (Gütschow et al 2018). The current lack of (Continued.) transparency and consistency, which requires com- % of 2015 pensation by tools and models supplied by academic global research, will hinder the NDC process. To move Region Countries emissions forward, we propose that the Principles of Transpar- Laos, Malaysia, Marshall Islands, ency and Consistency from the TACCC framework Micronesia, Mongolia, Myan- are extended to the framing of the NDCs themselves. mar, Nauru, New Zealand, Niue, This would be easy to achieve, by having countries Palau, Papua New Guinea, convert their pledges into clear emission targets Philippines, Samoa, Singapore, relative to the most recent available year in the data, Solomon Islands, Thailand, Timor-Leste, Tonga, Vanuatu, inclusive of all significant gases and sectors. Alterna- Vietnam tively, countries could deliver pledges in a flexible China China 24.9% format which then would be normalised by the Eastern Europe Albania, Armenia, Azerbaijan, 7.8% UNFCCC. An important detail is that the base year and Belarus, Bosnia and Herzego- should be consistent across all countries’ NDCs. In Central Asia vina, Georgia, Kazakhstan, effect, countries’ NDCs would then be normalized as Kyrgyzstan, Macedonia, Moldova, Montenegro, Russian we have done in our analysis (table 1), according to a Federation, Serbia, Tajikistan, category i style target. In our analysis we accomplished Turkmenistan, Ukraine, this normalisation comparing emissions to a consis- Uzbekistan tent base year of 2015, the most recently available base Latin America Antigua and Barbuda, Argentina, 8.4% year in the PRIMAP dataset. Going forward, as and Bahamas, Barbados, Belize, countries update their NDCs this base year should be Caribbean Bolivia, Brazil, Chile, Colombia, updated, to keep pledges relevant for the next period. Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador, Not only can this help produce targets of greater El Salvador, Grenada, Guate- ambition that are more open to external scrutiny, but mala, Guyana, Haiti, Honduras, it also will assist in improving effectiveness through Jamaica, Mexico, Panama, Para- minimising counterproductive systemic effects. Nor- guay, Peru, Saint Kitts and malisation of pledges will also put pressure on Nevis, Saint Lucia, Saint Vincent delivering ambitious updates of pledges every five and Grenadines, Suriname, years under the Paris Agreement’s ratcheting Trinidad and Tobago, Uruguay, Venezuela mechanism. Middle East Algeria, Bahrain, Egypt, Iran, Iraq, 7.6% and North Israel, Jordan, Kuwait, Lebanon, Acknowledgments Africa Morocco, Oman, Qatar, Saudi Arabia, Tunisia, Turkey, United Arab Emirates, Yemen The authors acknowledge financial support from Northern Canada, United States 14.7% the Spanish Ministry of Science, Innovation and America Universities, through the ‘María de Maeztu’ program South Asia Afghanistan, Bangladesh, Bhutan, 7.5% for Units of Excellence (MDM-2015-0552), and from India, Maldives, Nepal, the European Research Council (ERC) under the Pakistan, Sri Lanka European Union’s Horizon 2020 research and inno- Sub Saharan Angola, Benin, Botswana, Burkina 7.2% Africa Faso, Burundi, Cameroon, Cape vation programme through an ERC Advanced Grant Verde, Central African Republic, (grant agreement no. 741 087). The authors thank Chad, Comoros, Congo (DRC), Stefan Drews for useful comments. Congo (Republic), Cote d’Ivoire, Djibouti, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Appendix A. Definition of regions Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, % of 2015 Morocco, Namibia, Niger, global Nigeria, Rwanda, Sao Tome Region Countries emissions and Principe, Senegal, Sey- chelles, Sierra Leone, Somalia, Asia-Pacific Australia, Brunei, Cambodia, Cook 13.5% South Africa, South Sudan, Island, Fiji, Indonesia, Japan, Sudan, Swaziland, Tanzania, Kiribati, Korea (Democratic Togo, Uganda, Zambia, Republic), Korea (Republic), Zimbabwe 8 Environ. Res. Lett. 14 (2019) 084008 (Continued.) (Continued.) % of 2015 Ranking group of 2015 % of 2015 global emission intensity per global Region Countries emissions capita Countries emissions EU+ European Union (28), Switzerland, 8.1% 40%–60% Panama, Algeria, 7.3% Iceland, Norway, Liechtenstein, Liechtenstein, South Monaco Sudan, Mexico, No INDC Libya, Nicaragua, Syria 0.4% Switzerland, Republic of submission Congo, Thailand, Tanzania, Cook Islands, Barbados, Turkey, Honduras, Iraq, Laos, Appendix B. Definition of groups ranked Colombia, Dominica, by emission intensity per capita Lebanon, Myanmar, Georgia, North Korea, Nauru, Zimbabwe, Ranking group of 2015 % of 2015 Solomon Islands, emission intensity per global Cambodia, Marshall capita Countries emissions Islands, Chad, Tunisia, Jamaica, Vietnam, Saint Highest 20% Qatar, Trinidad and Tobago, 31.7% Vincent and Grenadines, Brunei, Niue, United Arab Maldives, Monaco Emirates, Kuwait, Guyana, 20%–40% Jordan, Egypt, Saint Lucia, 10.2% Bahrain, Paraguay, Armenia, Morocco, Zambia, Botswana, Grenada, Cuba, Guinea, Canada, Mongolia, United Somalia, Democratic States, Saudi Arabia, Republic of Congo, Iceland, Australia, Oman, Moldova, Morocco, Turkmenistan, Russian Albania, Kyrgyzstan, Federation, Bolivia, Mauritania, Guatemala, Kazakhstan, Singapore, Mali, Vanuatu, New Zealand, Suriname, Swaziland, Pakistan, Equatorial Guinea, Korea, Benin, Madagascar, Rep. (South), Palau, Iran, Nigeria, Dominican Argentina, Central African Republic, Tonga, Burkina Republic, Venezuela, Faso, India, Belize Guinea-Bissau, Samoa, 60%–80% Israel, Japan, The Bahamas, 48.7% Lesotho, Togo, Senegal, South Africa, Belarus, El Salvador Indonesia, Namibia, Lowest 20% Sierra Leone, Djibouti, 1.7% Uruguay, China, Papua Tuvalu, Uganda, Niger, New Guinea, Bosnia and Yemen, Sri Lanka, Nepal, Herzegovina, Sudan, Eritrea, Micronesia, European Union (28), Philippines, Ethiopia, Cote Serbia, Malaysia, d’Ivoire, Ghana, Uzbekistan, Montenegro, Bangladesh, Tajikistan, Angola, Ukraine, Antigua Cape Verde, Gambia, and Barbuda, Brazil, San Liberia, Haiti, Afghanistan, Marino, Mauritius, Sao Tome and Principe, Norway, Seychelles, Comoros, Costa Rica, Cameroon, Saint Kitts and Malawi, Kiribati, Burundi, Nevis, Ecuador, Azerbaijan, Rwanda, Kenya, Chile, Fiji, FYR of Macedonia, Bhutan, Gabon Timor-Leste, Note. Countries are ranked by 2015 total GHGs emissions per Andorra, Peru capita. 9 Environ. Res. Lett. 14 (2019) 084008 Appendix C. Absolute emission values for region and pledge type groupings i. Absolute emission iv. Projects absent of Pledge type reduction ii. BAU reduction iii. Intensity reduction GHG-emission targets Region 2015 Conditional 2015 Conditional 2015 Conditional 2015 Conditional emissions 2030 emissions emissions 2030 emissions emissions 2030 emissions emissions 2030 emissions (MtCO2e) (MtCO2e) (MtCO2e) (MtCO2e (MtCO2e) (MtCO2e (MtCO2e) (MtCO2e Asia-Pacific 1855 1486 4060 5012 321 517 499 502 China 0 0 0 0 12 400 15 255 0 0 Eastern Europe 3433 4026 93 102 235 358 106 136 and Central Asia (EECA) Latin America and 1432 1287 2299 2522 35 168 402 475 Caribbean (LAC) Middle East and 0 0 1893 3183 41 42 1851 2726 North Africa (MENA) Northern 7345 5066 0 0 0 0 0 0 America South Asia 0 0 703 1579 2960 9433 78 110 Sub Saharan 476 298 2502 3112 0 0 615 639 Africa (SSA) EU+ 4017 3440 0 0 0 0 0 0 Global Total 18 558 15 603 11 552 15 512 15 991 25 772 3551 4588 Appendix D. Normalised conditional (I)NDCs for all countries. NDC 2015 greenhouse gas 2030 conditional greenhouse % change 2015 Country Submission category emissions (GtCO2e) gas emissions (GtCO2e) to 2030 Afghanistan NDC ii 33.3 42.7 28.2% Albania NDC ii 8.1 9.0 10.8% Algeria NDC iv 227.0 262.2 15.5% Andorra NDC ii 0.5 0.4 −24.0% Angola INDC ii 202.0 96.6 −52.2% Antigua and Barbuda NDC iv 0.7 0.6 −8.5% Argentina NDC ii 478.0 369.0 −22.8% Armenia NDC ii 9.3 5.1 −44.9% Australia NDC i 479.0 430.7 −10.1% Azerbaijan NDC i 58.8 51.5 −12.3% Bahamas, The NDC iv 3.8 4.2 10.4% Bahrain NDC iv 39.0 54.9 40.7% Bangladesh NDC ii 200.0 252.9 26.4% Barbados NDC i 1.6 1.5 −5.6% Belarus NDC i 90.6 103.7 14.4% Belize NDC iv 3.7 3.2 −15.0% Benin NDC ii 25.9 32.0 23.6% Bhutan NDC ii −1.3 −1.3 −6.7% Bolivia NDC iv 183.0 219.7 20.1% Bosnia and Herzegovina NDC ii 30.3 33.3 9.8% Botswana NDC i 50.9 43.3 −14.9% Brazil NDC i 1400.0 1,265.6 −9.6% Brunei INDC iv 15.8 17.9 13.0% Burkina Faso NDC ii 41.5 96.7 133.1% Burundi NDC ii 8.2 60.3 633.8% 10 Environ. Res. Lett. 14 (2019) 084008 (Continued.) NDC 2015 greenhouse gas 2030 conditional greenhouse % change 2015 Country Submission category emissions (GtCO2e) gas emissions (GtCO2e) to 2030 Cambodia NDC ii 59.7 16.3 −72.7% Cameroon NDC ii 142.0 188.1 32.5% Canada NDC i 805.0 565.6 −29.7% Cape Verde NDC iv 0.6 0.4 −38.9% Central African Republic NDC ii 48.2 75.7 57.1% Chad NDC ii 52.4 8.2 −84.3% Chile NDC iii 3.6 135.0 7468.5% China NDC iii 12,400.0 18,086.4 24.0% Colombia INDC ii 225.0 234.5 4.2% Comoros NDC ii 0.8 0.1 −89.1% Congo, DRC NDC ii 224.0 356.9 59.3% Congo, Republic NDC ii 27.8 12.3 −55.7% Cook Islands NDC i 0.1 0.0 −93.2% Cote d’Ivoire NDC ii 30.8 30.0 −2.6% Cuba NDC iv 35.3 35.8 1.5% Djibouti NDC ii 1.7 1.8 5.3% Dominica NDC i 0.3 0.2 −45.5% Dominican Republic NDC i 24.8 17.2 −30.7% Ecuador INDC iv 99.7 107.8 8.2% Egypt NDC iv 305.0 368.8 20.9% El Salvador NDC iv 13.1 10.9 −16.6% Equatorial Guinea INDC i 16.0 11.4 −29.0% Eritrea INDC ii 7.3 1.2 −83.3% Ethiopia NDC ii 135.0 145.0 7.4% European Union (28) NDC i 3,930.0 3,378.0 −14.0% Fiji NDC iv −0.6 −1.2 84.5% Gabon NDC ii −85.2 65.0 −176.3% Gambia NDC iv 2.3 3.3 42.6% Georgia NDC ii 17.0 28.7 68.9% Ghana NDC ii 35.2 40.7 15.5% Grenada NDC i 0.3 0.0 −100.3% Guatemala NDC ii 43.1 41.7 −3.3% Guinea NDC i 36.5 32.6 −10.6% Guinea-Bissau INDC iv 4.0 5.0 24.6% Guyana NDC iv 22.2 40.4 82.1% Haiti NDC ii 10.7 14.8 38.3% Honduras NDC ii 47.2 52.6 11.5% Iceland NDC i 6.7 3.7 −44.7% India NDC iii 2,960.0 8,545.4 228.7% Indonesia NDC ii 2430.0 3,245.2 33.5% Iran INDC ii 913.0 1,355.2 48.4% Iraq INDC ii 188.0 507.1 169.7% Israel NDC ii 82.7 81.4 −1.6% Jamaica NDC iv 10.2 13.9 36.1% Japan NDC i 1310.0 1,007.1 −23.1% Jordan NDC ii 30.3 53.9 77.8% Kazakhstan NDC i 299.0 288.0 −3.7% Kenya NDC ii 30.3 100.1 230.4% Kiribati NDC ii 0.1 0.0 −73.0% Korea, Dem. Rep. NDC ii 107.0 112.2 4.8% (North) Korea, Rep. (South) NDC ii 684.0 518.1 −24.3% Kuwait INDC iv 128.0 181.1 41.5% Kyrgyzstan INDC ii 16.0 10.7 −33.4% Laos NDC iv 32.7 18.9 −42.3% Lebanon INDC ii 26.8 30.8 14.9% Lesotho NDC iv 4.7 3.5 −26.6% Liberia INDC ii 5.1 5.7 11.8% Libya No submission N/A 87.6 95.9 9.5% Liechtenstein NDC i 0.2 0.1 −30.4% 11 Environ. Res. Lett. 14 (2019) 084008 (Continued.) NDC 2015 greenhouse gas 2030 conditional greenhouse % change 2015 Country Submission category emissions (GtCO2e) gas emissions (GtCO2e) to 2030 Macedonia, FYR INDC ii 12.7 15.7 23.6% Madagascar NDC ii 58.6 106.8 82.3% Malawi NDC iv 15.5 16.9 8.8% Malaysia NDC iii 236.0 661.2 107.0% Maldives NDC ii 1.4 2.5 76.6% Mali NDC ii 45.0 32.3 −28.3% Marshall Islands NDC i 0.2 0.1 −51.3% Mauritania NDC ii 11.2 14.6 30.7% Mauritius NDC ii 8.4 4.9 −41.6% Mexico NDC ii 707.0 622.7 −11.9% Micronesia NDC i 0.2 0.1 −49.3% Moldova INDC i 11.9 9.7 −18.1% Monaco NDC i 0.1 0.1 −36.0% Mongolia NDC ii 65.9 58.9 −10.6% Montenegro NDC i 4.7 4.5 −3.7% Morocco NDC ii 109.0 98.9 −9.3% Mozambique INDC iv 81.1 120.6 48.7% Myanmar NDC iv 228.0 301.0 32.0% Namibia NDC ii 22.6 2.6 −88.6% Nauru NDC iv 0.0 0.1 96.0% Nepal NDC iv 44.3 63.2 42.6% New Zealand NDC i 65.1 47.8 −26.6% Nicaragua No submission N/A 16.7 19.4 16.4% Niger NDC Ii 34.6 63.1 82.3% Nigeria NDC Ii 430.0 540.0 25.6% Niue NDC iv 0.1 0.1 38.3% Norway NDC i 33.1 31.6 −4.7% Oman INDC ii 82.6 88.7 7.4% Pakistan NDC ii 470.0 1,282.4 172.9% Palau NDC i 0.3 0.0 −94.2% Panama NDC iv 22.8 29.1 27.6% Papua New Guinea NDC iv 69.6 76.0 9.1% Paraguay NDC ii 184.0 332.8 80.9% Peru NDC ii 184.0 208.8 13.5% Philippines INDC iv 145.0 79.3 −45.3% Qatar NDC iv 186.0 314.7 69.2% Russian Federation INDC i 2,570.0 2,887.5 12.4% Rwanda NDC iv 8.0 13.7 72.6% Saint Kitts and Nevis NDC ii 0.3 0.5 60.2% Saint Lucia NDC ii 0.6 0.6 11.4% Saint Vincent and NDC ii 0.4 0.5 24.8% Grenadines Samoa NDC iv 0.4 0.4 −5.3% San Marino NDC i 0.2 0.2 0.3% Sao Tome and Principe NDC ii 0.2 0.2 −6.5% Saudi Arabia NDC iv 643.0 1,050.8 63.4% Senegal INDC ii 31.4 29.2 −6.9% Serbia NDC i 68.1 70.8 4.0% Seychelles NDC ii 0.6 0.5 −21.5% Sierra Leone NDC iv 13.8 18.6 34.5% Singapore NDC iii 84.5 50.5 −45.7% Solomon Islands NDC ii 2.4 0.0 −98.4% Somalia NDC iv 41.3 37.6 −8.8% South Africa NDC ii 540.0 614.0 13.7% South Sudan INDC iv 67.2 70.5 4.9% Sri Lanka NDC iv 33.3 47.0 41.1% Sudan NDC iv 309.0 285.6 −7.6% Suriname INDC iv 7.8 9.2 17.9% Swaziland NDC iv 3.3 3.6 9.7% Switzerland NDC i 46.6 26.7 −42.8% 12 Environ. Res. Lett. 14 (2019) 084008 (Continued.) NDC 2015 greenhouse gas 2030 conditional greenhouse % change 2015 Country Submission category emissions (GtCO2e) gas emissions (GtCO2e) to 2030 Syria No submission N/A 78.5 23.5 −70.0% Tajikistan NDC i 10.2 21.4 109.6% Tanzania INDC ii 299.0 299.7 0.2% Thailand NDC ii 382.0 471.0 23.3% Timor-Leste NDC iv 7.6 8.5 11.9% Togo NDC ii 15.6 27.6 77.1% Tonga NDC ii 0.2 0.2 −21.6% Trinidad and Tobago NDC ii 91.1 87.6 −3.9% Tunisia NDC iii 41.4 56.4 1.9% Turkey INDC ii 415.0 929.0 123.9% Turkmenistan NDC iv 106.0 135.8 28.1% Tuvalu NDC i 0.0 0.0 −79.8% Uganda NDC ii 72.0 60.3 −16.3% Ukraine NDC i 320.0 588.6 83.9% United Arab Emirates NDC iv 323.0 493.4 52.8% United States NDC i 6,540.0 4,497.8 −31.2% Uruguay NDC iii 30.9 47.3 104.0% Uzbekistan INDC iii 235.0 637.2 54.8% Vanuatu NDC iv 0.7 0.9 35.8% Venezuela NDC ii 328.0 556.0 69.5% Vietnam NDC ii 329.0 590.6 79.5% Yemen INDC ii 45.3 37.7 −16.8% Zambia NDC i 373.0 210.4 −43.6% Zimbabwe NDC iv 63.9 60.0 −6.0% Note. NDC Categories: i Absolute emission reduction target, ii ‘business as usual’ reduction iii reduction of emission intensity of GDP, and iv projects absent of GHG emission targets. The percentage growth in emissions appear very high in some countries such as Burundi, Chile and Kenya (634%, 7469% and 230%, respectively) due to inconsistencies in the accounting of 2015 emissions in the PRIMAP dataset used and country’s own accounting in the NDC. In particular, there seem to be irregularities in the accounting for emissions associated with land use, land use change and forestry (LULUCF) in the PRIMAP database. ORCID iDs Gütschow J, Jeffery L, Gieseke R and Gebel R 2018 The PRIMAP- hist national historical emissions time series (1850-2015) V. 1.2. 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