Future growth patterns of world regions – A GDP scenario approach

Global GDP projections for the 21st century are needed for the exploration of long-term global environmental problems, in particular climate change. Greenhouse gas emissions as well as climate change mitigation and adaption capacities strongly depend on growth of per capita income. However, long-term economic projections are highly uncertain. This paper provides five new long-term economic scenarios as part of the newly developed shared socio-economic pathways (SSPs) which represent a set of widely diverging narratives. A method of GDP scenario building is presented that is based on assumptions about technological progress, and human and physical capital formation as major drivers of long-term GDP per capita growth. The impact of these drivers differs significantly between different shared socio-economic pathways and is traced back to the underlying narratives and the associated population and education scenarios. In a highly fragmented world, technological and knowledge spillovers are low. Hence, the growth impact of technological progress and human capital is comparatively low, and per capita income diverges between world regions. These factors play a much larger role in globalization scenarios, leading to higher economic growth and stronger convergence between world regions. At the global average, per capita GDP is projected to grow annually in a range between 1.0% (SSP3) and 2.8% (SSP5) from 2010 to 2100. While this covers a large portion of variety in future global economic growth projections, plausible lower and higher growth projections may still be conceivable. The GDP projections are put into the context of historic patterns of economic growth (stylized facts), and their sensitivity to key assumptions is explored

An integrated assessment model with endogenous growth

We introduce endogenous directed technical change into numerical integrated climate and development policy assessment. We distinguish expenditures on innovation (R&D) and imitation (international technology spillovers) and consider the role of capital investment in creating and implementing new technologies. Our main contribution is to calibrate and numerically solve the model and to examine the model’s sensitivity. As an application, we assess a carbon budget-based climate policy and vary the beginning of energy-saving technology transfer. Accordingly, China is a main beneficiary of early technology transfer. Herein, our results highlight the importance of timely international technology transfer for efficiently meeting global emission targets. Most of the consumption gains from endogenous growth are captured in the baseline. Moreover, mitigation costs turn out to be insensitive to changes in most of the parameters of endogenous growth. A higher effectivity of energy-specific relative to labor-specific expenditures on innovation and imitation reduces mitigation costs, though

Integrated assessment modeling: Modules for cooperation

An integrated assessment (IA) model combines knowledge from very different disciplines in view of a practical problem. Most models developed so far are rather monolithic in the sense that it is difficult to combine components from different models for purposes of new assessments. We propose to develop a modular approach to IA based on advances in knowledge management as well as in object oriented software engineering. The incentive structure of modular IA is based on turning the knowledge produced neither into public nor into private, but rather into club goods. Competition amongst modelers becomes a process of discovery at the level of module design and module coupling, with strong synergies between competing teams. Together they develop a community pool of IAM-modules, along with software and know-how for running them in varying combinations

Combining ambitious climate policies with efforts to eradicate poverty

Climate change threatens to undermine efforts to eradicate extreme poverty. However, climate policies could impose a financial burden on the global poor through increased energy and food prices. Here, we project poverty rates until 2050 and assess how they are influenced by mitigation policies consistent with the 1.5 °C target. A continuation of historical trends will leave 350 million people globally in extreme poverty by 2030. Without progressive redistribution, climate policies would push an additional 50 million people into poverty. However, redistributing the national carbon pricing revenues domestically as an equal-per-capita climate dividend compensates this policy side effect, even leading to a small net reduction of the global poverty headcount (−6 million). An additional international climate finance scheme enables a substantial poverty reduction globally and also in Sub-Saharan Africa. Combining national redistribution with international climate finance thus provides an important entry point to climate policy in developing countries

REMIND2.1: transformation and innovation dynamics of the energy-economic system within climate and sustainability limits

This paper presents the new and now open-source version 2.1 of the REgional Model of INvestments and Development (REMIND). REMIND, as an integrated assessment model (IAM), provides an integrated view of the global energy–economy–emissions system and explores self-consistent transformation pathways. It describes a broad range of possible futures and their relation to technical and socio-economic developments as well as policy choices. REMIND is a multiregional model incorporating the economy and a detailed representation of the energy sector implemented in the General Algebraic Modeling System (GAMS). It uses non-linear optimization to derive welfare-optimal regional transformation pathways of the energy-economic system subject to climate and sustainability constraints for the time horizon from 2005 to 2100. The resulting solution corresponds to the decentralized market outcome under the assumptions of perfect foresight of agents and internalization of external effects. REMIND enables the analyses of technology options and policy approaches for climate change mitigation with particular strength in representing the scale-up of new technologies, including renewables and their integration in power markets. The REMIND code is organized into modules that gather code relevant for specific topics. Interaction between different modules is made explicit via clearly defined sets of input and output variables. Each module can be represented by different realizations, enabling flexible configuration and extension. The spatial resolution of REMIND is flexible and depends on the resolution of the input data. Thus, the framework can be used for a variety of applications in a customized form, balancing requirements for detail and overall runtime and complexity

Exploring the possibility space: taking stock of the diverse capabilities and gaps in integrated assessment models

Abstract Integrated assessment models (IAMs) have emerged as key tools for building and assessing long term climate mitigation scenarios. Due to their central role in the recent IPCC assessments, and international climate policy analyses more generally, and the high uncertainties related to future projections, IAMs have been critically assessed by scholars from different fields receiving various critiques ranging from adequacy of their methods to how their results are used and communicated. Although IAMs are conceptually diverse and evolved in very different directions, they tend to be criticised under the umbrella of ‘IAMs’. Here we first briefly summarise the IAM landscape and how models differ from each other. We then proceed to discuss six prominent critiques emerging from the recent literature, reflect and respond to them in the light of IAM diversity and ongoing work and suggest ways forward. The six critiques relate to (a) representation of heterogeneous actors in the models, (b) modelling of technology diffusion and dynamics, (c) representation of capital markets, (d) energy-economy feedbacks, (e) policy scenarios, and (f) interpretation and use of model results.</jats:p

Development of a Fuzzy optimization model, supporting global warming decision-making

An increasing number of models have been developed to support global warming response policies. The model constructors are facing a lot of uncertainties which limit the evidence of these models. The support of climate policy decision-making is only possible in a semi-quantitative way, as presented by aFuzzy model. The model design is based on an optimization approach, integrated in a bounded risk decision-making framework. Given some regional emission-related and impact-related restrictions, optimal emission paths can be calculated. The focus is not only on carbon dioxide but on other greenhouse gases too. In the paper, the components of the model will be described. Cost coefficients, emission boundaries and impact boundaries are represented asFuzzy parameters. TheFuzzy model will be transformed into a computational one by using an approach of Rommelfanger. In the second part, some problems of applying the model to computations will be discussed. This includes discussions on the data situation and the presentation, as well as interpretation of results of sensitivity analyses. The advantage of theFuzzy approach is that the requirements regarding data precision are not so strong. Hence, the effort for data acquisition can be reduced and computations can be started earlier. Copyright Kluwer Academic Publishers 1996Decision-making, climate policy-making, global warming, cost-effectiveness analysis, modelling, fuzzy logic, risk aversion,