Achievability of the Paris targets in the EU—the role of demand-side-driven mitigation in different types of scenarios

The Paris Agreement includes “pursuing efforts to limit the global temperature rise to 1.5 °C” for the first time and the target of greenhouse gas neutrality during the twenty-first century (UNFCCC 2015). While many global mitigation scenarios limit global warming to well below 2 °C until 2100, only a few scenarios achieve the 1.5 °C limit (Rogelj et al. 2015). The latter require large amounts of negative CO₂ emissions in the second half of the century. However, there is a substantial risk that CO₂ removal technologies may not be able to provide negative emissions to the extent required in these scenarios (Kartha and Dooley 2016).

When assessing the drivers of mitigation, it is common to decompose the CO₂ emissions into the product of an activity variable, the associated energy intensity (energy use per activity) and the associated CO₂ intensity (CO₂ emissions per energy used). According to the IPCC’s 5th assessment report (AR5), it is a consensus in the literature that the reduction of both CO₂ and energy intensities plays a key role in every mitigation scenario that is compatible with limiting global warming to below 2 °C (IPCC 2014). While reducing energy intensities is a main lever in the short to medium term, in particular, this is less clear in the long term. Based on an index decomposition and a comparison with historical trends, Peters et al. (2017) argue that the reductions of CO₂ intensities are larger than the reductions of energy intensities in very ambitious mitigation scenarios in the long term. Lechtenboehmer et al. (2017) provide strong arguments that reducing energy intensity via higher energy efficiency will remain an important lever, not only for those end-uses that cannot be electrified but also for electricity itself. Furthermore, there is evidence that also limiting the activities has to be addressed based on sufficiency considerations, because reducing energy intensities cannot ensure the reduction of energy demand in absolute terms (Mundaca et al. 2013), in particular because of rebound effects (Sorrell 2010).

This article compares the reductions of the carbon intensities and the energy use in different sectors within the European Union (EU) for a set of mitigation scenarios that may comply with the Paris targets and conducts an in-depth analysis of the long-term demand-side mitigation options (sufficiency, energy efficiency, electrification, and fuel switching). This set includes national, European, and global scenarios as well as scenarios based on bottom-up and top-down approaches to modeling end-use sectors. We evaluate bottom-up scenarios for the EU, France, Germany, Italy, and the UK to determine sector-specific mitigation rates and compare them to the EU’s pathway in scenarios with a European or global focus. To understand the reasons behind the different evolution of carbon intensities and energy use in the various scenarios, we apply an IDA at the level of sectors (energy supply, industry, buildings, and transport), i.e., we decompose the sectoral evolution of CO₂ emissions. These reasons can be manifold, e.g., the electrification of end-use sectors affects both carbon intensities and energy use. The IDA makes it possible to single out these impacts. From this, we derive conclusions about the indicators and policies for an EU mitigation strategy that is compatible with the 1.5 °C target.

The analysis focuses on the EU because—against the backdrop of the current emissions as well as the economic and political capabilities—the EU can play a pioneering role in demonstrating that the Paris Agreement’s goals are indeed achievable. The EU’s current target of reducing greenhouse gas (GHG) emissions by 80–95% until 2050 compared to 1990 is based on 2 °C-compatible emission pathways. There is evidence that the EU has some leeway in choosing such a pathway (see, e.g., Wachsmuth et al. 2015), while for the 1.5 °C target, it can only be said that emission reductions at the lower end of the range of 80–95% by the year 2050 are very likely to be insufficient (Schleussner et al. 2016). At the same time, however, many EU member states have developed advanced national decarbonization scenarios.

The remainder of this paper is organized as follows. The next section provides an overview of the relevant literature and identifies gaps that we aim to close. Then, we explain the methodology of our IDA and the main assumptions. We introduce the set of scenarios that are assessed in this article and provide descriptive results on the development of carbon and energy intensities. In the results section, we present and discuss the findings of the IDA at sector level and comment on the methodology. In the concluding section, we relate our findings to gaps in the literature and the EU’s policies regarding the 1.5 °C target.

There is a wide literature concerning the application of IDA to decompose the drivers of carbon emissions, with the majority of studies looking at historical emissions. A review can be found in Xu and Ang (2013). Here, we focus on papers that have applied index or structural decompositions in a prospective manner to scenarios for the entire energy system and/or all of the end-use sectors:

In the international context, mitigation pathways are usually identified using global integrated assessment models (IAMs), which are based on top-down assumptions concerning the emission dynamics of mostly aggregated end-use sectors. Our analysis at the level of individual national economies follows Fortes et al. (2013) and Förster et al. (2013), whose sectoral results suggest that IAM-based findings should be complemented by an analysis of bottom-up models. We therefore apply an IDA to all EU end-use sectors in global and European mitigation scenarios as well as the most ambitious national scenarios for the four largest emitters in the EU (Germany, France, Italy, and the UK). To the best of our knowledge, an IDA has not yet been applied in a detailed comparison of all end-use sectors of mitigation scenarios compatible with the Paris targets. This makes it possible to check the plausibility of the findings from IAMs and to identify concrete measures based on more detailed statements about the required technologies and structural changes. This is the first main novel contribution of this article.

This research also addresses a methodological issue. The decomposition of carbon emissions into the product of an activity variable, the energy intensity, and the CO₂ intensity assumes the independence of the three factors. It is well known that the activity variable and the energy intensity may not be truly independent due to rebound effects (see, e.g., Sorrell 2010), i.e., lower energy intensity may cause higher activity. Vice versa, decreasing activities may also entail a growth in energy intensity, as it was the case during the economic crisis in 2009 (Jotzo et al. 2012). An additional methodological problem seemingly not yet addressed in the literature is that the independence of carbon and energy intensities assumed in IDA may be weak, in particular in scenarios with emissions close to net zero or even below. With regard to this issue, it is instructive to assume that the reduction of energy intensities in a mitigation scenario reaches historical trends only. Then the same overall emission reductions would require huge amounts of additional non-fossil energy (Lechtenboehmer et al. 2017). Moreover, even to sustain the carbon intensity reduction (and thus the associated emission reductions) would require substantial additional non-fossil energy. The standard IDA approaches are ignorant of this effect. A discussion of this methodological issue and the associated sensitivities of the results are the second novel contribution of this article.

To identify the main levers for the reduction of carbon emissions in the evaluation of national and international mitigation scenarios, we separate the impacts of demographic and economic development from the reductions of carbon and energy intensities using an index decomposition analysis of the energy-related carbon emissions (cf. Capros et al. 2014).

As Fortes et al. (2013) and Förster et al. (2013) pointed out the importance of sectoral details, we consider the disaggregation of carbon emissions both for the energy system as a whole and for the end-use sectors separately. Given the different levels of disaggregation in the various scenarios, we decided to look at industry as a whole and subsume freight and passenger transport under “transport” as well as the residential and service sector under “buildings.” This leaves us with three end-use sectors: industry, buildings, and transport (see Fig. 7 in the annex).

In the transport sector, we include aviation and domestic shipping but exclude international shipping because there is no uniform approach to its coverage in the evaluated scenarios. In the industry sector, we look at energy-related emissions only and only include the energy-related share of industrial CCS.

Electrification means that an important share of emissions is shifted from the end-use sectors to the energy supply sector. We therefore also carry out a complementary IDA for the energy supply side. Here, we focus on the gross electricity demand including combined heat and power (CHP) generation and transmission losses. We do not look at the centralized generation of district heat because it plays only a minor role in the scenarios.

Since our focus is on end-use sectors, we present the methodology for them in detail here. It is relatively straightforward to adapt this to the energy supply sector. In general, index decompositions can be carried out with regard to different metrics, in particular primary energy and final energy, but also more sophisticated metrics such as exergy. We choose final energy for the following reasons. Due to the statistical conventions for the primary energy of solar, wind, and hydropower, a naive approach to primary energy would result in mixing the impacts of renewable energy sources (RES) and energy efficiency. The so-called substitution approach to primary energy supply makes it possible to circumvent this issue. Still, the substitution approach cannot single out the important contributions of electrification and synthetic fuels, as neither shows up on the level of primary energy demand. We cannot apply an exergy-based approach because most of the scenarios lack a sufficient level of detail. A caveat of this is that there is no account of exergy losses due to the conversion of higher-value to lower-value energy carriers, e.g., the application of power-to-heat technologies.

The general methodology of an IDA of carbon emissions and in particular the commonly used logarithmic mean Divisia index (LMDI) approach are described in Xu and Ang (2013). LMDI has the advantage that the decomposition yields no residual term. In the context of scenarios with CCS, the problem arises of how to deal with zero and negative values of CO₂ intensities (Ang and Liu 2007a, b). The solution for negative values provided by Ang and Liu (2007b) results in allocating all emission reductions to CCS. In this article, we single out the impact of the use of CCS and instead look at the gross CO₂ emissions C_{i, t} of sector i at time t instead of the net CO₂ emissions:where CCS_{i, t} is the amount of sectoral carbon emissions avoided by CCS.¹ Applying the commonly used Kaya identities, we decompose the net carbon emissions of sector i at time t aswhere FED_{i, t} is the sectoral final energy demand and AI_{i, t}, EI_{i, t}, CI_{i, t} are the sectoral activity, energy, and carbon intensity at time t. We note that in the decomposition applied here, CCS does not affect the carbon intensity of either electricity or fossil fuels because it is treated separately. This avoids negative carbon intensities and thus the drawback of approaches like the one followed by Ang and Liu (2007b), where no account is taken of changes in the non-negative drivers at the point in time when carbon intensities become negative.

Since the carbon emissions of electricity and heat supply are accounted for in the IDA of the energy supply sector, we do not cover indirect emissions in the end-use sectors and set the carbon intensity of electricity and heat to zero. Moreover, we assume that no carbon emissions are associated with the use of renewable fuels and hydrogen. Then the carbon intensity can be further decomposed aswhere \( {CI}_{i,t}^{\mathrm{foss}} \) is the sectoral carbon intensity of the fossil fuels used and \( {s}_{i,t}^j \) are the sectoral shares of the energy source j (electricity, district heat, hydrogen, and renewable fuels + heat (ambient heat, biomass, and synfuels)) at time t.

Löfgren and Muller (2010) have contributed several important methodological remarks: For instance, they point out that monetary activity variables can at best be proxies for real sectoral activity. In addition, they underline the sensitivity of an IDA to the level of aggregation of both sectors and time steps, something that has been stressed in the literature several times. They conclude that an IDA should avoid monetary variables like value added and disaggregate sectors and time steps if possible. Following Löfgren and Muller (2010), we use the highest time disaggregation possible in order to justify the approximation via an index decomposition that assumes independence of the factors. In our case, we have intermediate time steps of 10 years available for all the evaluated scenarios. The changes in carbon emissions are therefore disaggregated aswhere ∆C_{i, t1, t2} is the change in carbon emissions from time t2 to t1. In order to avoid a residual, we apply the LMDI approach mainly developed by Ang and co-authors (see, e.g., Xu and Ang 2013). The LMDI formula for the contribution of the intensity variables in sector i readswhere \( \mathrm{L}\left(y,z\right)=\frac{y-z}{\ln (y)-\ln (z)}\kern0.75em \) is the logarithmic mean of y and z, and X is either the activity, the energy, or the fossil carbon intensity. The LMDI formula for the contributions of changes in the final energy mix is

To understand the impact of introducing time steps, consider the following stylized but insightful example: a reduction of emissions by 100% can be achieved by reducing energy intensity by 50% and reducing carbon intensity to zero. The relevant question is now how much of the emission reduction should be allocated to the reduction of energy intensity. If one thinks of the reduction of carbon and energy intensity as happening one after the other, the order is crucial:

An index decomposition without time steps cannot distinguish between Variant A and B. In both cases, the LMDI approach attributes roughly 85% to carbon intensity, thereby assuming that decreasing carbon intensity makes it less and less useful to reduce energy intensity. Given the transportability and the vanishing marginal costs of non-biomass RES, however, it seems likely that the reduction of energy intensities will reduce the use of RES only to a limited extent. When the supply with non-fossil energy approaches final energy demand in the long run of a decarbonization scenario, one might even expect that the amount of non-fossil fuels supplied is expanded only until the remaining demand is met. Building up RES overcapacities beyond necessary levels of redundancy is even more unlikely because it will be ambitious to establish a full supply with non-fossil fuels even for the reduced levels of demand due to limitations because of land use and acceptance issues.

Another critical point in index decompositions is the choice of the activity variable. As Löfgren and Muller (2010) argue, monetary variables can lead to inconvenient sensitivities. These can be with respect to exchange rates but also to impacts of the intensity changes on the monetary variables like the value added by the energy sector. Since some of the scenarios evaluated here are not based on monetary variables at all and we want to avoid such sensitive assumptions, we look at the energy use per capita instead of considering the energy intensity relative to an economic activity for the overall analysis. On the sectoral level, various other activity variables are applied in the literature:

In summary, the consistent use of population size for all the sectors enables a clear comparison between them. Due to the lack of individual activity variables, however, the impact of limiting activity is an implicit part of the energy use per capita in the buildings and industry sector, while it is explicitly separated in the transport sector. For the other sectors, this means it is not possible to separate the contributions of energy efficiency from those due to sufficiency in a quantitative way. We therefore discuss this qualitatively for the national scenarios.

Given the limited availability of global scenarios compatible with the Paris target and the remaining ambiguity on the European level, we start with scenarios that have more than two thirds likelihood of keeping the temperature rise below 2 °C during the whole of the twenty-first century (no overshoot of the 2 °C target). The European scenarios selected apply a fixed cumulative emission budget for 2010 to 2050 that is compatible with the majority of global 2 °C scenarios.

The evaluated scenarios are required to provide specific data for the EU as well as on a sectoral level. They also have to cover at least all energy-related carbon emissions. Sources that provided sufficiently detailed information and were therefore included are as follows:

For the national scenarios, we focused on the most ambitious scenarios available for the four largest emitters in the EU, namely

All the national scenarios reduce carbon emissions by at least 80% from 1990 to 2050. However, they differ significantly in their level of ambition, with a reduction of energy-related carbon emissions by 83% for Italy, by 93% for France, by 97% for Germany and by 100% for the UK from 2010 to 2050.⁴ The evaluated national scenarios are target-oriented normative scenarios, but they all consider only mitigation options that are either already mature or at least close to technological maturity. In addition, the German and Italian scenarios are based on the techno-economic modeling of climate policies. All the national scenarios rule out the construction of any new nuclear plants and thus result in a phase-out of nuclear. The French and UK scenarios determine energy demand based on sufficiency and look at technical feasibility as well as impacts on job creation, while the German and Italian scenarios limit sufficiency considerations and apply cost-optimization models that achieve the desired level of decarbonization via an increasing carbon price. In 2015, the four countries considered accounted for approximately 53% of both the EU’s population and its GHG emissions.

The EU’s energy use per capita and the related carbon intensity span a wide range in the evaluated global and European mitigation scenarios. For the national mitigation scenarios, the energy uses and carbon intensities start out at differing values depending on the individual countries’ circumstances. For example, carbon intensities are lower in France because of its high share of nuclear in the electricity mix. With regard to emission intensities, the differences reflect the varying levels of ambition of the national mitigation scenarios. Conversely, energy uses converge to a similar level in all the evaluated national mitigation scenarios in the long run (see Fig. 1).

Comparing the carbon intensities for the EU between scenarios based on global and those based on European models does not reveal any significant difference between the two groups. The development depends mainly on the scenario assumptions and less on the type of model used. In the European models, the carbon intensities of final energy fall by 61 to 83% by 2050, which is well within the range of the global models of 41 to 96%. In all scenarios, carbon intensities decrease rapidly and approach zero or even become negative in the second half of the twenty-first century. The difference here is mainly due to different technology assumptions in the scenarios, in particular, the availability of CCS. The carbon intensities in the national scenarios show a decrease ranging from 70 to 100%, with a gradual convergence of the existing national differences.

The differences are more pronounced for the energy use per capita: In the scenarios based on global models, the development of the energy use per capita ranges widely from an increase by 5% to a decrease by 47% until 2050. The European scenarios are all within the bottom half of this range (25–38%). The national scenarios show a similar decrease of 26 to 36% until 2030 and end up between 46 and 62% in 2050. In particular, the least ambitious national scenario has a similar level of reduction as the most ambitious supra-national scenario. We recall that the energy use covers both the impacts of sufficiency-driven activity reductions and efficiency-driven intensity reductions. Both with regard to sufficiency and efficiency, we emphasize that there may be various reasons for the higher ambition of national scenarios, e.g., additional technology options, different demand and activity constraints as well as varying cost and diffusion assumptions. To identify the reasons for the differences, it is necessary to take a closer look at the end-use sectors (see the next section). After 2050, the range of reductions in the global models widens even more with reductions of more than 60% that are not realized before 2080.

To contextualize the reductions of energy uses and carbon intensities in the scenarios, we discuss them with respect to historical trends and the most relevant literature. Mundaca and Markandya (2016) assessed the progress made in decarbonizing energy systems on the level of world regions between 1972 and 2012. Globally, they find that the reduction of carbon and energy intensities has not been able to compensate fully for the growth in population and economic activity. In particular, no absolute reductions of energy demand have been achieved. OECD Europe is the only region that has made progress after 2005 in this respect. Nevertheless, Mundaca and Markandya (2016) find that the simple continuation of historical trends until 2050 would fail to reach the target of an 80% reduction of CO₂ emissions in OECD Europe by between 3 and 21 percentage points (depending on the development of activities). The latter suggests that limiting the growth of activities can also provide an important contribution. Spencer et al. (2017) compared the reduction of sectoral carbon and energy intensities in a set of mitigation of scenarios to the historical trends for the same countries considered here. While the mean annual reductions of sectoral energy intensities and the carbon intensity of electricity since 2000 range from 0.5 to 1.8%, the sectoral carbon intensities have only reduced by 0.3 to 0.6% on average per year (Spencer et al. 2017). When comparing these sectoral trends to the required rates in 2020–2030, they find substantial gaps with regard to the median intensities in the end-use sectors, with the biggest gap of 2.3 percentage points per year for the carbon intensity of residential buildings. The gap for the carbon intensity of electricity supply is even higher at almost 4 percentage points. When we carry out the same comparison for the national scenarios considered here, we find similar but on average slightly larger gaps, with the maximum gap between annual reductions of 2.7 percentage points for the end-use sectors and a gap of 4.4 percentage points for electricity supply. The differences reflect that the overall reduction of carbon emissions is only slightly higher than 80% in the scenarios in Spencer et al. (2017). In all cases, the gaps between current trends and required rates show how challenging it is to realize the corresponding mitigation pathways. However, it is important to note that the required ranges of intensity reductions have been observed in the recent past, at least for some years in certain EU countries (Spencer et al. 2017).

In the remainder of this paper, we apply an IDA to explore in more detail the factors underlying the relatively similar decrease of carbon intensities, and the faster decrease of energy use per capita. As this requires more detailed consideration of the sector and variable definitions in the models, we select one global and one European scenario: namely, the IMAGE model run for the AMPERE2-450-NucOff-OPT scenario and the PRIMES model run for the AMPERE5-HiEffHiRES scenario (see Table 1 for an overview of all scenarios included in the analysis). This focus on two specific scenarios also allows a detailed discussion of the similarities and differences in the results. These two scenario runs were selected based on the following hierarchical criteria:

The latter two criteria ensure that the scenarios are as similar as possible to the national scenarios. Consequently, the differences between the national bottom-up models and the more aggregated models can be identified more clearly.

Table 2 summarizes the definitions of all the variables used in the index decompositions as well as any necessary data adjustments. Note that the definitions imply that any efficiency gains due to electrification are accounted for in the energy use per capita. Accordingly, the share of electricity (in the following “electrification”) only accounts for the related change in carbon intensity. For heat pumps, we take into account the ambient heat delivered, so that their main contribution occurs under “renewable fuels + heat.”

As pointed out, the independence of carbon and energy intensity assumed in an IDA is an approximation that is valid only within a small range around the carbon and energy intensities at a given point in time, i.e., for small changes of the intensities. In mitigation scenarios, however, the changes in both carbon and energy intensities are substantial.

With regard to this issue, comparing the index composition for 2010 to 2050 with and without intermediate time steps reveals some interesting differences. As a particularly meaningful example, we focus on the transport sector (see Fig. 6), but similar effects can be observed in the other sectors. The results of the IDA of both approaches for all sectors are provided in the supplementary online material.

While the total reduction in the transport sector is not changed when including time steps by definition, there are significant changes in the impact of specific levers. In particular, the contribution of reducing energy intensities is rated higher for all evaluated scenarios if time steps are included. In contrast, the contributions of electrification and RES fuels are mostly rated lower. For the activity variable, the impacts are rated higher when using the time-step approach. These effects reflect that activity and intensity changes have a much higher impact when the CO₂ intensity is still high. All these effects are stronger for the more ambitious scenarios and the highest differences occur for energy intensity in the German scenario (29%) and in the UK scenario (33%). This shows that the lack of independence between energy intensities and the other levers becomes critical in the context of net-zero emissions. The changes for population, hydrogen, and fossil carbon intensity are comparably low (therefore not shown in Fig. 6).

Mathematically speaking, looking at net-zero CO₂ emissions means regarding a singular point in time, when CO₂ emissions fully decouple from their main drivers. The suggested separation of emission reductions via CCS in Eq. (1) at least avoids crossing this singular point and thus the inversion of the drivers’ impacts. By adding intermediate time steps in Eq. (5), the order of changes during one time step is reduced and so the validity of the approximation is improved for each of the stepwise decompositions. The comparison of the results with and without time steps suggests that an IDA underrates the contribution of lowering energy intensities when the carbon intensity becomes marginal. Our approach does not remove this problem fully, but reduces it significantly, as it only occurs in the final time step. A more detailed analysis of the critical time step is a task for future research.

Finally, we note that the assumed independence of carbon and energy intensity may not be a reasonable approximation in mitigation scenarios even in the shorter term. This is the case for example with regard to electricity. Due to the priority dispatch for RES, a reduction in electricity demand will not reduce the amount of RES used at all. The opposite case, where the independence holds true, is the case of a RES quota, as currently applied in the EU’s transport sector. A reduction of fuel use leaves the RES quota unchanged and thus, it reduces the total amount of biofuels used. In the longer run, when the RES shares increases, both the priority dispatch and RES quotas are likely not to persist. Nevertheless, those differences underline that short-term IDA should consider sectoral particularities of the dependence of energy and carbon intensities, too.

For the existing global mitigation scenarios that are consistent with the most ambitious Paris target of limiting global warming to 1.5 °C, it is deeply uncertain whether negative emissions can be provided to the extent required (Kartha and Dooley 2016). In this article, we have shown that national mitigation scenarios based on bottom-up modeling contain plausible reductions of carbon emissions in end-use sectors that are more ambitious than in the more aggregated scenarios.

This suggests that the high cumulative negative emissions in the global mitigation scenarios compatible with the 1.5 °C target can be significantly reduced by very ambitious demand-side mitigation strategies (sufficiency, energy efficiency, electrification, and fuel switching). We analyze the impacts of stronger demand-side mitigation on the EU’s carbon budget in detail in a forthcoming companion paper (Duscha et al. 2018).

Due to the limited regional scope and time horizon, the national scenarios cannot replace the IAM-based design of pathways compatible with the Paris targets. On the other hand, it is difficult for global IAMs to address regional heterogeneity, in particular with respect to certain demand-side options (e.g., material cycles). Therefore, national bottom-up scenarios may help to judge and improve the plausibility of the global pathways. The sector results clearly identify several important differences between the national and the regionally more aggregated scenarios, among others, additional technology options and sufficiency-based demand reduction. However, the lack of sectoral activity variables for some of the scenarios prevented a complete separation of the contributions of energy efficiency and sufficiency, which both play an important role in keeping the 1.5 °C target achievable. We therefore recommend a higher disaggregation of scenarios, in particular, including additional sectoral activity variables in scenario databases, e.g., production values for energy-intensive goods.

Since the evaluated national scenarios are limited to emission reductions before 2050 and the extensive use of CCS is excluded for normative reasons in several scenarios, these scenarios have to push other mitigation options close to their technical limits in order to reach their targets. This may pose an obstacle to their realization. However, the scenarios only consider feasible mitigation options that are at least close to market maturity.

On the policy side, early action with regard to both energy efficiency and sufficiency is necessary in order to realize the ambitious reduction of carbon emissions found in the national scenarios thereby fostering a pathway compatible with the 1.5 °C target (cf. Wachsmuth et al. 2015):

Peters et al. (2017) argue that the reductions of carbon intensities compared to historical trends are more substantial than the reductions of energy intensities in highly ambitious decarbonization scenarios. Our discussion of methodological aspects suggests that scenarios based on regionally aggregated models may at least partly underestimate the contributions of reducing energy use, in particular, if they apply a top-down approach to modeling energy demand. Moreover, standard index decomposition approaches may produce misleading results when applied to scenarios with emissions close to net zero or even below, because they do not reflect that emission intensities are likely to be much higher in the case of higher energy intensities. As discussed, using the highest available disaggregation of sectors and time steps limits this problem, but cannot fully remove it. We thus suggest interpreting the results from index decompositions of low-carbon scenarios with care and addressing the dependence of carbon and energy intensities as well as sectoral activities in future research.

Furthermore, Peters et al. (2017) have come up with a set of key indicators for tracking progress with regard to the Paris targets based on an index decomposition. They place large emphasis on the supply side covering CCS, fossil fuel switching, and various RES. On the demand side, in contrast, they only track the overall energy intensity per GDP. Having discussed both the additional mitigation options on the supply side and the dependence of carbon intensity reductions on energy intensity reductions, we suggest tracking the demand side in more detail as well:

Wilson et al. (2012) point out that the innovation efforts supporting demand-side-driven mitigation are substantially lower than those supporting supply-side-driven mitigation in spite of partially higher impacts. In summary, our analyses complement the findings of Wilson et al. (2012) by showing that the importance of demand-side mitigation options is underrated in long-term scenario analysis as well. To keep the demand for renewable energy within acceptable boundaries, reducing energy use will continue to be crucial even in the case of negative carbon intensities, which are required by all available mitigation pathways compatible with the 1.5 °C target.