Social impacts of climate change mitigation policies and their implications for inequality

The Paris Agreement and the Sustainable Development Goals (SDGs) set ambitious targets for environmental, economic and social progress. Climate change mitigation policies play a central role in this process. To maximize the benefits and minimize the negative effects of climate change mitigation policies, policymakers need to be aware of the indirect and often complex social and inequality impacts that these policies may have and the pathways through which these impacts emerge. Better understanding of the distributional and inequality impacts is important to avoid negative social and distributional outcomes as countries ratchet up their climate policy ambition in the post-Paris context. This paper synthesizes evidence from the existing literature on social co-impacts of climate change mitigation policy and their implications for inequality. The analysis shows that most policies are linked to both co-benefits and adverse side-effects, and can compound or lessen inequalities depending on contextual factors, policy design and policy implementation. The risk of negative outcomes is greater in contexts characterized by high levels of poverty, corruption and economic and social inequalities, and where limited action is taken to identify and mitigate potentially adverse side-effects.

Key policy insights

• The risk of adverse social outcomes associated with climate change mitigation policies, including worsening inequality, increases as countries ratchet up their ambition to meet the Paris Agreement targets. Many policies that have so far only been piloted will need to be up-scaled.

• Negative inequality impacts of climate policies can be mitigated (and possibly even prevented), but this requires conscious effort, careful planning and multi-stakeholder engagement. Best results can be achieved when potential inequality impacts are taken into consideration in all stages of policy making, including policy planning, development and implementation.

• Climate change mitigation policies should take a pro-poor approach that, in best case scenarios, can also lead to a reduction of existing inequalities.

     

Potential climate change impacts on the probability of wind damage in a south Swedish forest

We estimated how the possible changes in wind climate and state of the forest due to climate change may affect the probability of exceeding critical wind speeds expected to cause wind damage within a forest management unit located in Southern Sweden. The topography of the management unit was relatively gentle and the forests were dominated by Norway spruce (Picea abies (L.) Karst.). We incorporated a model relating the site index (SI) to the site productivity into the forest projection model FTM. Using estimated changes in the net primary production (NPP) due to climate change and assuming a relative change in NPP equal to a relative change in the site productivity, we simulated possible future states of the forest under gradual adjustment of SI in response to climate change. We estimated changes in NPP by combining the boreal-adapted BIOMASS model with four regional climate change scenarios calculated using the RCAO model for the period 2071–2100 and two control period scenarios for the period 1961–1990. The modified WINDA model was used to calculate the probability of wind damage for individual forest stands in simulated future states of the forest. The climate change scenarios used represent non-extreme projections on a 100-year time scale in terms of global mean warming. A 15–40% increase in NPP was estimated to result from climate change until the period 2071–2100. Increasing sensitivity of the forest to wind was indicated when the management rules of today were applied. A greater proportion of the calculated change in probability of wind damage was due to changes in wind climate than to changes in the sensitivity of the forest to wind. While regional climate scenarios based on the HadAM3H general circulation model (GCM) indicated no change (SRES A2 emission scenario) or a slightly reduced (SRES B2 emission scenario) probability of wind damage, scenarios based on the ECHAM4/OPYC3 GCM indicated increased probability of wind damage. The assessment should, however, be reviewed as the simulation of forest growth under climate change as well as climate change scenarios are refined.     

Potential consequences of projected climate change impacts on hydroelectricity generation

There is a growing concern that countries should reduce their dependence on fossil fuels for electricity generation and look to other cleaner technologies. Hydroelectricity is one such option. However, given that hydropower is dependent on rainfall and associated runoff for power generation, it is susceptible to both the positive and negative impacts of climate change, such as increases in temperature and changes in precipitation and runoff. In this paper, impacts on hydropower generation have been organised as either changes in long-term trends or short-term variability and shocks. These impacts could either manifest themselves as direct impacts on hydropower generation potential or as indirect impacts (or ancillary impacts) such as increased competition for water. Citing examples from around the world, this paper investigates the scale of these projected impacts, and the potential cost implication of inaction. It concludes by making recommendations for possible adaptive options to build resilience in response to local impacts.     

Potential impacts of afforestation on climate change and extreme events in Nigeria

Afforestation is usually thought as a good approach to mitigate impacts of warming over a region. This study presents an argument that afforestation may have bigger impacts than originally thought by previous studies. The study investigates the impacts of afforestation on future climate and extreme events in Nigeria, using a regional climate model (RegCM3), forced with global climate model simulations. The impacts of seven afforestation options on the near future (2031–2050, under A1B scenario) climate and the extreme events are investigated. RegCM3 replicates essential features in the present-day (1981–2000) climate and the associated extreme events, and adequately simulates the seasonal variations over the ecological zones in the country. However, the model simulates the seasonal climate better over the northern ecological zones than over the southern ecological zones. The simulated spatial distribution of the extreme events agrees well with the observation, though the magnitude of the simulated events is smaller than the observed. The study shows that afforestation in Nigeria could have both positive and negative future impacts on the climate change and extreme events in the country. While afforestation reduces the projected global warming and enhances rainfall over the afforested area (and over coastal zones), it enhances the warming and reduces the rainfall over the north-eastern part of the country. In addition, the afforestation induces more frequent occurrence of extreme rainfall events (flooding) over the coastal region and more frequent occurrence of heat waves and droughts over the semi-arid region. The positive and negative impacts of the afforestation are not limited to Nigeria; they extend to the neighboring countries. While afforestation lowers the warming and enhances rainfall over Benin Republic, it increases the warming and lowers the rainfall over Niger, Chad and Cameroon. The result of the study has important implication for the ongoing climate change mitigation and adaptation efforts in Nigeria.     

Optimal filtering for Bayesian detection and attribution of climate change

In the conventional approach to the detection of an anthropogenic or other externally forced climate change signal, optimal filters (fingerprints) are used to maximize the ratio of the observed climate change signal to the natural variability noise. If detection is successful, attribution of the observed climate change to the hypothesized forcing mechanism is carried out in a second step by comparing the observed and predicted climate change signals. In contrast, the Bayesian approach to detection and attribution makes no distinction between detection and attribution. The purpose of filtering in this case is to maximize the impact of the evidence, the observed climate change, on the prior probability that the hypothesis of an anthropogenic origin of the observed signal is true. Whereas in the conventional approach model uncertainties have no direct impact on the definition of the optimal detection fingerprint, in optimal Bayesian filtering they play a central role. The number of patterns retained is governed by the magnitude of the predicted signal relative to the model uncertainties, defined in a pattern space normalized by the natural climate variability. Although this results in some reduction of the original phase space, this is not the primary objective of Bayesian filtering, in contrast to the conventional approach, in which dimensional reduction is a necessary prerequisite for enhancing the signal-to-noise ratio. The Bayesian filtering method is illustrated for two anthropogenic forcing hypotheses: greenhouse gases alone, and a combination of greenhouse gases plus sulfate aerosols. The hypotheses are tested against 31-year trends for near-surface temperature, summer and winter diurnal temperature range, and precipitation. Between six and thirteen response patterns can be retained, as compared with the one or two response patterns normally used in the conventional approach. Strong evidence is found for the detection of an anthropogenic climate change in temperature, with some preference given to the combined forcing hypothesis. Detection of recent anthropogenic trends in diurnal temperature range and precipitation is not successful, but there remains strong net evidence for anthropogenic climate change if all data are considered jointly.

---

Multi-pattern fingerprint method for detection and attribution of climate change

 The multi-variate optimal fingerprint method for the detection of an externally forced climate change signal in the presence of natural internal variability is extended to the attribution problem. To determine whether a climate change signal which has been detected in observed climate data can be attributed to a particular climate forcing mechanism, or combination of mechanisms, the predicted space–time dependent climate change signal patterns for the candidate climate forcings must be specified. In addition to the signal patterns, the method requires input information on the space–time dependent covariance matrices of the natural climate variability and of the errors of the predicted signal patterns. The detection and attribution problem is treated as a sequence of individual consistency tests applied to all candidate forcing mechanisms, as well as to the null hypothesis that no climate change has taken place, within the phase space spanned by the predicted climate change patterns. As output the method yields a significance level for the detection of a climate change signal in the observed data and individual confidence levels for the consistency of the retrieved climate change signal with each of the forcing mechanisms. A statistically significant climate change signal is regarded as consistent with a given forcing mechanism if the statistical confidence level exceeds a given critical value, but is attributed to that forcing only if all other candidate climate change mechanisms (from a finite set of proposed mechanisms) are rejected at that confidence level. Although all relations can be readily expressed in standard matrix notation, the analysis is carried out using tensor notation, with a metric given by the natural-variability covariance matrix. This simplifies the derivations and clarifies the invariant relation between the covariant signal patterns and their contravariant fingerprint counterparts. The signal patterns define the reduced vector space in which the climate trajectories are analyzed, while the fingerprints are needed to project the climate trajectories onto this reduced space. Received: 19 April 1996/Accepted: 21 April 1997     

Making sense of the politics in the climate change loss & damage debate

The Warsaw International Mechanism for Loss and Damage (L&D) associated with Climate Change Impacts (WIM) was established in 2013 to advance i) knowledge generation; ii) coordination and iii) support to address losses and damages under the UNFCCC. So far, the work undertaken by the WIM Executive Committee (ExCom) has focused on enhancing understanding and awareness of the issue and promoting collaboration with relevant stakeholders. Delivering on the WIM’s third function on action and support has lagged behind, and ‘the political’ nature of L&D has often been blamed for this. Key terrains of contention among Parties have included the positioning of L&D governance vis-à-vis the adaptation space and struggles around state liability and compensation. As a way to facilitate discussion on implementation options, recent research has suggested de-politicising aspects of the L&D debate; yet we have very little insight into how the politics are understood within the realm of international L&D governance. This paper brings an analysis of ‘the political’ into the picture by identifying the complex and underlying issues that fuel contention within UNFCCC L&D negotiations. It gives centre stage to the way different framings of norms and material interests affect the debate, and challenges the tendency in current L&D literature to overlook the socio-historical and political underpinnings of this area of policy-making. We employ a qualitative multi-methods research design which draws on content analysis of 138 official Parties’ submissions and statements, 14 elite interviews with key current and former L&D negotiators and is built on a foundation of 3 years of participant observation at COPs and WIM meetings. We approach this data with a political ethnographic sensibility that seeks to explore how meanings are constructed within and across different sources of data. Our empirical results show that, rather than being a monolithic dispute, L&D catalyses different yet intertwined unresolved discussions. We identify five areas of contention, including continued disputes around compensation; conflicts on the legitimacy of L&D as a third pillar of climate action; tensions between the technical and political dimension of the debate; debates over accountability for losses and damages incurred; and the connection of L&D with other unresolved issues under the Convention.     

Classifying knowledge on climate change impacts, adaptation, and vulnerability in Europe for informing adaptation research and decision-making: A conceptual meta-analysis

Knowledge on climate change impacts, adaptation, and vulnerability is fragmented, because it is found in disparate case studies which use inconsistent terminology and focus on distinct aspects relevant to adaptation. While large-scale syntheses such as the Assessment Reports of the Intergovernmental Panel on Climate Change provide a high level overview and are useful for international decision-making, there is a need for systematic and flexible access to this research-based knowledge in order to aid future adaptation research and decision-making. Against this background, we present a ‘conceptual’ meta-analysis, a novel approach to meta-analyse studies on climate change impacts, adaptation, and vulnerability in Europe. The meta-analysis results in a classification scheme for relating the diverse studies. This scheme consists of (i) a classification of studies according to the type of adaptation-relevant results they produce and (ii) a hierarchical classification of the regional and thematic context of studies. The implementation of this scheme, for example in the form of a database, overcomes some of the identified gaps of current adaptation knowledge representation. We furthermore present a quantitative analysis of the classified studies that exemplifies how the developed classification scheme can be applied to get a systematic and quantitative overview of the knowledge they contain. Thus, the conceptual meta-analysis and the classification scheme represent a first step towards a systematisation of knowledge on climate change impacts, adaptation, and vulnerability and may be seen as a useful complement to qualitative literature reviews.     

Simple indices of global climate variability and change Part II: attribution of climate change during the twentieth century

Five simple indices of surface temperature are used to investigate the influence of anthropogenic and natural (solar irradiance and volcanic aerosol) forcing on observed climate change during the twentieth century. These indices are based on spatial fingerprints of climate change and include the global-mean surface temperature, the land-ocean temperature contrast, the magnitude of the annual cycle in surface temperature over land, the Northern Hemisphere meridional temperature gradient and the hemispheric temperature contrast. The indices contain information independent of variations in global-mean temperature for unforced climate variations and hence, considered collectively, they are more useful in an attribution study than global mean surface temperature alone. Observed linear trends over 1950–1999 in all the indices except the hemispheric temperature contrast are significantly larger than simulated changes due to internal variability or natural (solar and volcanic aerosol) forcings and are consistent with simulated changes due to anthropogenic (greenhouse gas and sulfate aerosol) forcing. The combined, relative influence of these different forcings on observed trends during the twentieth century is investigated using linear regression of the observed and simulated responses of the indices. It is found that anthropogenic forcing accounts for almost all of the observed changes in surface temperature during 1946–1995. We found that early twentieth century changes (1896–1945) in global mean temperature can be explained by a combination of anthropogenic and natural forcing, as well as internal climate variability. Estimates of scaling factors that weight the amplitude of model simulated signals to corresponding observed changes using a combined normalized index are similar to those calculated using more complex, optimal fingerprint techniques.     

A conceptual framework for cross-border impacts of climate change

Climate change impacts, adaptation and vulnerability studies tend to confine their attention to impacts and responses within the same geographical region. However, this approach ignores cross-border climate change impacts that occur remotely from the location of their initial impact and that may severely disrupt societies and livelihoods. We propose a conceptual framework and accompanying nomenclature for describing and analysing such cross-border impacts. The conceptual framework distinguishes an initial impact that is caused by a climate trigger within a specific region. Downstream consequences of that impact propagate through an impact transmission system while adaptation responses to deal with the impact propagate through a response transmission system. A key to understanding cross-border impacts and responses is a recognition of different types of climate triggers, categories of cross-border impacts, the scales and dynamics of impact transmission, the targets and dynamics of responses and the socio-economic and environmental context that also encompasses factors and processes unrelated to climate change. These insights can then provide a basis for identifying relevant causal relationships. We apply the framework to the floods that affected industrial production in Thailand in 2011, and to projected Arctic sea ice decline, and demonstrate that the framework can usefully capture the complex system dynamics of cross-border climate impacts. It also provides a useful mechanism to identify and understand adaptation strategies and their potential consequences in the wider context of resilience planning. The cross-border dimensions of climate impacts could become increasingly important as climate changes intensify. We conclude that our framework will allow for these to be properly accounted for, help to identify new areas of empirical and model-based research and thereby support climate risk management.     

Macroeconomic analysis of the employment impacts of future EU climate policies

This article gives a detailed account of part of the modelling that was carried out for the assessment of the EU's proposed energy and climate targets for 2030. Using the macro-econometric simulation model, E3ME, and drawing on results from the PRIMES energy systems model, it shows that a 40% reduction in GHG emissions (compared to 1990 levels) could lead to an increase in employment of up to 0.7 million jobs in Europe. Furthermore, if the same GHG reduction target was combined with targets for renewables and energy efficiency, the net increase in jobs could be as high as 1.2 million. Both results are in contrast to the standard findings from computable general equilibrium (CGE) models, reflecting the different underlying assumptions (e.g. labour supply) to the modelling approach. Additional sensitivity testing shows that the ways in which the energy efficiency and renewable measures are funded are important factors in determining overall economic impact.

Policy relevance

In recent years there has been much debate as to whether the European Union should have a single GHG reduction target or a set of targets that also cover renewables and energy efficiency. This paper elaborates on part of the modelling that was carried out for the official assessment of the European Union's proposed energy and climate targets for 2030. Using an empirical, model-based approach, it compares a scenario where there is a single 40% GHG reduction target to a scenario that also includes a 30% renewables target and stricter energy efficiency standards. The model results show that the large investment stimulus needed to meet the combined targets leads to higher levels of GDP and employment. This suggests that there could be medium-term economic and social benefits to including all three targets in the future energy and climate package.     

Assessing climate change impacts in the European north

Global climate change and its regional manifestation will result in significant impacts in the European North. However, in order to determine the consequences of such impacts, a holistic, integrated assessment is needed. This paper sets the stage for the remainder of this volume by describing an attempt to derive such an assessment for the Barents Sea Region through the EU-funded BALANCE project. The paper explains some of the major methodologies employed in the study. It also provides insight into major results obtained and attempts to answer a number of overarching questions. It will be shown that climate change does present a significant threat to environmental and societal integrity in the study region. However, it will also be shown that stakeholders regard other drivers of future changes (economical, political developments) at least as equally important for their personal lives.     

Potential impacts of land-use on climate variability and extremes

This study aims at exploring potential impacts of land-use vegetation change (LUC) on regional climate variability and extremes. Results from a pair of Australian Bureau of Meteorology Research Centre (BMRC) climate model 54-yr (1949-2002) integrations have been analysed. In the model experiments, two vegetation datasets are used, with one representing current vegetation coverage in China and the other approximating its potential coverage without human intervention. The model results show potential impacts ...     

Optimal detection and attribution of climate change: sensitivity of results to climate model differences

Fingerprint techniques for the detection of anthropogenic climate change aim to distinguish the climate response to anthropogenic forcing from responses to other external influences and from internal climate variability. All these responses and the characteristics of internal variability are typically estimated from climate model data. We evaluate the sensitivity of detection and attribution results to the use of response and variability estimates from two different coupled ocean atmosphere general circulation models (HadCM2, developed at the Hadley Centre, and ECHAM3/LSG from the MPI für Meteorologie and Deutsches Klimarechenzentrum). The models differ in their response to greenhouse gas and direct sulfate aerosol forcing and also in the structure of their internal variability. This leads to differences in the estimated amplitude and the significance level of anthropogenic signals in observed 50-year summer (June, July, August) surface temperature trends. While the detection of anthropogenic influence on climate is robust to intermodel differences, our ability to discriminate between the greenhouse gas and the sulfate aerosol signals is not. An analysis of the recent warming, and the warming that occurred in the first half of the twentieth century, suggests that simulations forced with combined changes in natural (solar and volcanic) and anthropogenic (greenhouse gas and sulfate aerosol) forcings agree best with the observations.