Complexity and the Economics of Climate Change: A Survey and a Look Forward

Balint, Tomas; Lamperti, Francesco; Mandel, Antoine; Napoletano, Mauro; Roventini, Andrea; Sapio, Alessandro

doi:10.1016/j.ecolecon.2017.03.032

Cited by 146 publications

(55 citation statements)

References 167 publications

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“…With respect to the relations between networks and economic policies, network analysis techniques are well suited to explore the direct and indirect effects of policy interventions (Haldane, 2014), as they represent a nonpareil informative tool for the policymaker dealing with macro-prudential regulation (Haldane, 2009;Farmer et al, 2012;Battiston et al, 2016;Gaffeo and Molinari, 2016), trade policy (Gala et al, 2018;Giammetti et al, 2019;Giammetti, 2019), climate policy (Balint et al, 2017;Vega and Mandel, 2018), fiscal policy (Briganti et al, 2018). Furthermore, with respect to the specific field of monetary policy, the Bank of England's chief economist calls for an understanding of the complex international monetary network dynamics as a pre-requisite for effective management of monetary policies (Haldane 2014).…”

Section: Network Analysis Business Cycle and Monetary Policy: A Shormentioning

confidence: 99%

Monetary policy, crisis and capital centralization in corporate ownership and control networks: A B-Var analysis

Brancaccio

Giammetti

Lopreite

et al. 2019

Structural Change and Economic Dynamics

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Based on a connection between network analysis and B-VAR models, this paper provides a first empirical evidence of the relationships between capital centralization expressed in terms of network control on one hand and monetary policy guidelines and business cycles on the other. Our findings suggest that a tightening monetary policy leads to a decrease in the fraction of top shareholders of network control which results in a higher centralization of capital; and that a higher centralization of capital, in turn, leads to a reduction of GDP with respect to its trend. These relations are confirmed both for the United States and the Euro Area.

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Section: Network Analysis Business Cycle and Monetary Policy: A Shormentioning

confidence: 99%

Monetary policy, crisis and capital centralization in corporate ownership and control networks: A B-Var analysis

Brancaccio

Giammetti

Lopreite

et al. 2019

Structural Change and Economic Dynamics

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show abstract

“…The monetized impacts of climate change, known as social cost of carbon (SCC), are used by national agencies and/or international organizations in cost-benefit analyses of climate change regulations and programs (Watkiss and Hope 2011). Nevertheless, subsequent critiques have challenged the findings of IAMs arguing that the models employ strong simplifications and use weakly defended assumptions; the socio-economic scenarios usually under-sample the range of plausible futures; the models do not incorporate emissions of any greenhouse gas other than carbon dioxide; etc., e.g., (Sterner and Persson 2008;O'Neill 2010;Warren et al 2010;Masur and Posner 2011;van Vuuren et al 2011;Hof et al 2012;Kopp and Mignone 2012;Metcalf and Stock 2015;Pindyck 2013;Balint et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Embedding Preference Uncertainty for Environmental Amenities in Climate Change Economic Assessments: A “Random” Step Forward

et al. 2019

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While there is a considerable debate regarding the choice of proper discount rates for assessing climate change projects and policies, only a tiny body of literature emphasizes “what to discount”. Usually, climate change economic assessments rely on tools and methods that employ strong simplifications, assuming, among others, given and fixed preferences about the values of man-made and environmental goods. Aiming to fill a gap in the literature, this paper leaves aside the issue of discounting and focuses on the nature and impact of preference uncertainty on the economic estimates of future climate change damages on ecosystem non-market goods and services. To this end, a general random walk-based stochastic model is proposed, combining a number of parameters, e.g., the growth of income, depletion of environmental assets, the elasticity of income and demand, and the change in preferences towards the environment. The illustrative application of the model shows that the value of environmental losses is significantly affected by the change in preferences. By doing so, the model allows the analyst to visualize future paths of preference evolutions and to bring future values of damaged environmental assets realistically to the fore. If these elements are neglected when estimating climate change-related future damages to environmental goods and services, the results may be too narrow from a policy perspective.

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“…Moreover, high-end climate change requires transformative solutions and, accordingly, tools that address multiple feedbacks, irreversibility, non-linearity and tipping points (Tàbara et al 2018). The necessary simultaneous assessment of policy processes, applied practices, drivers, impacts and uncertainties can be supported by dynamic modelling, which also allows for the exploration of various socio-economic and environmental scenarios (Balint et al 2017). Crucially, such an approach enables the representation of institutional actions, the omission of which may lead to biased or misleading simulation results (Parker et al 2003;Manson 2005).…”

Section: Introductionmentioning

confidence: 99%

Modelling dynamic effects of multi-scale institutions on land use change

2018

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Formal institutions impact on the dynamics of land use change through their objectives, actions and decision-making processes in response to socioeconomic or environmental changes such as climate change. The effects and interplay of these actions are not fully understood, and hence, institutional processes are rarely integrated in modelling analyses of land use change. The complex effects of institutional interactions within land systems can be better understood through modelling approaches that address the heterogeneity of the institutional actors involved and how their decisions affect temporal and spatial dynamics. In this paper, we present an agent-based model of autonomous land managers interacting with institutional agents at two spatial scales. We explore different parameters of institutional intervention (subsidy rate, triggers for action, delay in monitoring, scale-based precedence) under socioeconomic drivers and analyse key metrics such as the maximum over-and undersupply of ecosystem services, connectivity of land uses, and degree of change in land use patterns. Levels of subsidy and action triggers have the greatest impact on the magnitude of land use change and the maximum oversupply of ecosystem services. In terms of achieving institutional objectives, subsidy rate is the most important single parameter in the model. Furthermore, we find non-linearity with variations in parameters that have important implications for current land use change modelling and requirements for empirical studies of land use planning institutions. Finally, the effects of high-end climate change may require entirely novel institutional behaviours.

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Complexity and the Economics of Climate Change: A Survey and a Look Forward

Cited by 146 publications

References 167 publications

Monetary policy, crisis and capital centralization in corporate ownership and control networks: A B-Var analysis

Monetary policy, crisis and capital centralization in corporate ownership and control networks: A B-Var analysis

Embedding Preference Uncertainty for Environmental Amenities in Climate Change Economic Assessments: A “Random” Step Forward

Modelling dynamic effects of multi-scale institutions on land use change

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