Search Results
Understanding Socio-metabolic Inequalities Using Consumption Data from Germany
The Earth’s population of seven billion consume varying amounts of planetary resources with varying impacts on the environment. We combine the analytical tools offered by the socio-ecological metabolism and class theory and contribute to a novel social stratification theory to identify the differences in individual resource and energy use. This approach is applied to German society, we use per capita greenhouse gas emissions (GHG) as a proxy for resource and energy use and investigate socio-metabolic characteristics of individuals from an economic, social and cultural perspective. The results show large disparities and inequalities in emission patterns in the German society. For example, the GHG in the lowest and highest emission groups can differ by a magnitude of ten. Income, education, age, gender and regional differences (Eastern vs. Western Germany) result in distinct emission profiles. We question the focus on individual behavioral changes and consumption choices to reduce carbon emissions instead of structural changes through political decisions. We argue that emission differences are directly linked to the effects of inequalities and class differences in capitalist societies. Our research results show that natural resource and energy consumption are important for explaining social differentiation in modern societies.
Scientific assessments to facilitate deliberative policy learning
Putting the recently adopted global Sustainable Development Goals or the Paris Agreement on international climate policy into action will require careful policy choices. Appropriately informing decision-makers about longer-term, wicked policy issues remains a considerable challenge for the scientific community. Typically, these vital policy issues are highly uncertain, value-laden and disputed, and affect multiple temporal and spatial scales, governance levels, policy fields, and socioeconomic contexts simultaneously. In light of this, science-policy interfaces should help facilitate learning processes and open deliberation among all actors involved about potentially acceptable policy pathways. For this purpose, science-policy interfaces must strive to foster some enabling conditions: (1) “representation” in terms of engaging with diverse stakeholders (including experts) and acknowledging divergent viewpoints; (2) “empowerment” of underrepresented societal groups by co-developing and integrating policy scenarios that reflect their specific knowledge systems and worldviews; (3) “capacity building” regarding methods and skills for integration and synthesis, as well as through the provision of knowledge synthesis about the policy solution space; and (4) “spaces for deliberation”, facilitating direct interaction between different stakeholders, including governments and scientists. We argue that integrated, multi-stakeholder, scientific assessment processes—particularly the collaborative assessments of policy alternatives and their various implications—offer potential advantages in this regard, compared with alternatives for bridging scientific expertise and public policy. This article is part of a collection on scientific advice to governments.
Archetype analysis in sustainability research: Methodological portfolio and analytical frontiers
In sustainability research, archetype analysis reveals patterns of factors and processes that repeatedly shape social-ecological systems. These patterns help improve our understanding of global concerns, including vulnerability, land management, food security, and governance. During the last decade, the portfolio of methods used to investigate archetypes has been growing rapidly. However, these methods differ widely in their epistemological and normative underpinnings, data requirements, and suitability to address particular research purposes. Therefore, guidance is needed for systematically choosing methods in archetype analysis. We synthesize strengths and weaknesses of key methods used to identify archetypes. Demonstrating that there is no “one-size-fits-all” approach, we discuss advantages and shortcomings of a range of methods for archetype analysis in sustainability research along gradients that capture the treatment of causality, normativity, spatial variations, and temporal dynamics. Based on this discussion, we highlight seven analytical frontiers that bear particular potential for tackling methodological limitations. As a milestone in archetype analysis, our synthesis supports researchers in reflecting on methodological implications, including opportunities and limitations related to causality, normativity, space, and time considerations in view of specific purposes and research questions. This enables innovative research designs in future archetype analysis, thereby contributing to the advancement of sustainability research and decision-making.
Measuring Success: Improving Assessments of Aggregate Greenhouse Gas Emissions Reduction Goals
Long-term success of the Paris Agreement will depend on the effectiveness of the instruments that it sets in place. Key among these are the nationally determined contributions (NDCs), which elaborate country-specific goals for mitigating and adapting to climate change. One role of the academic community and civil society in supporting the Paris Agreement is to assess the consistency between the near-term action under NDCs and the agreement's long-term goals, thereby providing insight into the chances of long-term success. Here we assess the strengths and weaknesses of current methods to estimate the effectiveness of the mitigation component of NDCs and identify the scientific and political advances that could be made to improve confidence in evaluating NDCs against the long-term goals. Specifically, we highlight (1) the influence of post-2030 assumptions on estimated 21st century warming, (2) uncertainties arising from the lack of published integrated assessment modeling scenarios with long-term, moderate effort reflecting a continuation of the current political situation, and (3) challenges in using a carbon budget approach. We further identify aspects that can be improved in the coming years: clearer communication regarding the meaning, likelihood, and timeframe of NDC consistent warming estimates; additional modeling of long-term, moderate action scenarios; and the identification of metrics for assessing progress that are not based solely on emissions, such as infrastructure investment, energy demand, or installed power capacity.
Global bilateral migration projections accounting for diasporas, transit and return flows, and poverty constraints
BACKGROUND Anticipating changes in international migration patterns is useful for demographic studies and for designing policies that support the well-being of those involved. Existing forecasting methods do not account for a number of stylized facts that emerge from large-scale migration observations and theories: existing migrant communities - diasporas - act to lower migration costs and thereby provide a mechanism of self-amplification; return migration and transit migration are important components of global migration flows; and poverty constrains emigration. OBJECTIVE Here we present hindcasts and future projections of international migration that explicitly account for these nonlinear features. METHODS We develop a dynamic model that simulates migration flows by origin, destination, and place of birth. We calibrate the model using recently constructed global datasets of bilateral migration. RESULTS We show that the model reproduces past patterns and trends well based only on initial migrant stocks and changes in national incomes. We then project migration flows under future scenarios of global socioeconomic development. CONCLUSIONS Different assumptions about income levels and between-country inequality lead to markedly different migration trajectories, with migration flows either converging towards net zero if incomes in presently poor countries catch up with the rest of the world; or remaining high or even rising throughout the 21st century if economic development is slower and more unequal. Importantly, diasporas induce significant inertia and sizable return migration flows. CONTRIBUTION Our simulation model provides a versatile tool for assessing the impacts of different socioeconomic futures on international migration, accounting for important nonlinearities in migration drivers and flows.
Topology of products similarity network for market forecasting
The detection and prediction of risk in financial markets is one of the main challenges of economic forecasting, and draws much attention from the scientific community. An even more challenging task is the prediction of the future relative gain of companies. We here develop a novel combination of product text analysis, network theory and topological based machine learning to study the future performance of companies in financial markets. Our network links are based on the similarity of firms’ products and constructed using the Securities Exchange Commission (SEC) filings of US listed firms. We find that several topological features of this network can serve as good precursors of risks or future gain of companies. We then apply machine learning to network attributes vectors for each node to predict successful and failing firms. The resulting accuracies are much better than current state of the art techniques. The framework presented here not only facilitates the prediction of financial markets but also provides insight and demonstrates the power of combining network theory and topology based machine learning.
Do Benefits from Dynamic Tariffing Rise? Welfare Effects of Real-Time Retail Pricing Under Carbon Taxation and Variable Renewable Electricity Supply
We analyze the gross welfare gains from real-time retail pricing in electricity markets where carbon taxation induces investment in variable renewable technologies. Applying a stylized numerical electricity market model, we find a U-shaped association between carbon taxation and gross welfare gains. The benefits of introducing real-time pricing can accordingly be relatively low at relatively high carbon taxes and vice versa. The non-monotonous change in welfare gains can be explained by corresponding changes in the inefficiency arising from “under-consumption” during low-price periods rather than by changes in wholesale price volatility. Our results may cast doubt on the efficiency of ongoing roll-outs of advanced meters in many electricity markets, since net benefits might only materialize at relatively high carbon tax levels and renewable supply shares. © 2019, The Author(s).
Sustainable Development Goals (SDGs): Are we successful in turning trade-offs into synergies?
The Agenda 2030 with its 17 Sustainable Development Goals (SDGs) provides the framework that all United Nations (UN) member states have pledged to fulfill. The achievement of this agenda crucially depends on whether humankind will be able to maximize synergies and resolve existing trade-offs between the SDGs. We provide the first analysis of future interactions for projected SDG trends until 2030 within and between goals, and we analyze how trade-offs and synergies have evolved in the recent past globally. For certain goals, we find positive developments with notable synergies in our projections, especially for SDGs 1, 3, 7, 8, and 9: Poverty alleviation and strengthening the economy, rooted in innovation, and modern infrastructure, therefore continue to be the basis upon which many of the other SDGs can be achieved. However, especially SDGs 11, 13, 14, 16, and 17 will continue to have notable trade-offs, as well as non-associations with the other goals in the future, which emphasizes the need to foster innovations and policies that can make our cities and communities more sustainable, as well as strengthen institutions and spur climate action. We show examples of a successful transformation of trade-offs into synergies that should be emulated in other areas to create a virtuous cycle of SDG progress. The alarming inability to overcome certain persistent trade-offs we have found, and indeed the deterioration for some SDGs, can seriously threaten the achievement of the Agenda 2030.
Extending Near-Term Emissions Scenarios to Assess Warming Implications of Paris Agreement NDCs
In the Paris Agreement countries have agreed to act together to hold global warming well below 2°C over preindustrial levels and to pursue efforts to limit warming to 1.5°C. To assess if the world is on track to meet this long-term temperature goal, countries' pledged emissions reductions (Nationally Determined Contributions, NDCs) need to be analyzed for their implied warming. Several research groups and nongovernmental organizations have estimated this warming and arrived at very different results but have invariably concluded that the current pledges are inadequate to hold warming below 2°C, let alone 1.5°C. In this paper we analyze different methods to estimate 2100 global mean temperature rise implied by countries' NDCs, which often only specify commitments until 2030. We present different methods to extend near-term emissions pathways that have been developed by the authors or used by different research groups and nongovernmental organizations to estimate 21st century warming consequences of Paris Agreement commitments. The abilities of these methods to project both low and high warming scenarios in line with the scenario literature is assessed. We find that the simpler methods are not suitable for temperature projections while more complex methods can produce results consistent with the energy and economic scenario literature. We further find that some methods can have a strong high or low temperature bias depending on parameter choices. The choice of methods to evaluate the consistency of aggregated NDC commitments is very important for reviewing progress toward the Paris Agreement's long-term temperature goal.
The strategic dimension of financing global public goods
One challenge in addressing transboundary problems such as climate change is the incentive to free-ride. Transfers from multilateral compensation funds are often used to counteract such incentives, albeit with varying success. We examine how such funds can change the incentive to free-ride in a global public-goods game. In our game, self-interested countries choose their own preferred course, deciding their voluntary public good provision, whether to join a fund that offers compensation for providing the public good and the volume of compensatory payments. We show that (i) total public-good provision is higher when those contributing are given more compensation; and (ii) non-participation in the fund can be punished if the remaining members decrease their public-good provision sufficiently. We then examine three specific fund designs. In the first, the compensation paid to each country is equal to the percentage of above-average total costs for public-goods provision. This design is best able to deter free-riding and can establish the social optimum as the equilibrium. In the second, the compensation paid to each country is a function of the marginal cost of their public-good provision. Here there are significant incentives to free-ride. In the third case, the monetary resources provided by the fund are fixed, a design frequently encountered in international funds. This design is the one least able to deter free-riding. © 2020 The Author(s)