Using relatively simple and widely used models, we illustrate how carbon emission concerns could be integrated into operational decision-making with regard to procurement, production, and inventory management. We show how, by associating carbon emission parameters with various decision variables, traditional models can be modified to support decision-making that accounts for both cost and carbon footprint. We examine how the values of these parameters as well as the parameters of regulatory emission control policies affect cost and emissions. We use the models to study the extent to which carbon reduction requirements can be addressed by operational adjustments, as an alternative (or a supplement) to costly investments in carbon-reducing technologies. We also use the models to investigate the impact of collaboration among firms within the same supply chain on their costs and carbon emissions and study the incentives firms might have in seeking such cooperation. We provide a series of insights that highlight the impact of operational decisions on carbon emissions and the importance of operational models in evaluating the impact of different regulatory policies and in assessing the benefits of investments in more carbon efficient technologies. Note to Practitioners-Firms worldwide, responding to the threat of government legislation or to concerns raised by their own consumers or shareholders, are undertaking initiatives to reduce their carbon footprint. It is the conventional thinking that such initiatives will require either capital investments or a switch to more expensive sources of energy or input material. In this paper, we show that firms could effectively reduce their carbon emissions without significantly increasing their costs by making only operational adjustments and by collaborating with other members of their supply chain. We describe optimization models that can be used by firms to support operational decision making and supply chain collaboration, while taking into account carbon emissions. We analyze the effect of different emission regulations, including strict emission caps, taxes on emissions, cap-and-offset, and cap-and-trade, on supply chain management decisions. In particular, we show that the presence of emission regulation can significantly increase the value of supply chain collaboration.

https://www.sutd.edu.sg/cmsresource/idc/papers/2013_Carbon_Footprint_and_the_Management_of_Supply_Chains_Insights_From_Simple_Models.pdf

Carbon Footprint and the Management of Supply Chains: Insights From Simple Models

Assessing Transformation Pathways

This paper uses age-at-school-entry policies to identify the effect of female education on fertility and infant health. We focus on sharp contrasts in schooling, fertility, and infant health between women born just before and after the school entry date. School entry policies affect female education and the quality of a woman%u2019s mate and have generally small, but possibly heterogeneous, effects on fertility and infant health. We argue that school entry policies manipulate primarily the education of young women at risk of dropping out of school.

https://papers.ssrn.com/sol3/Delivery.cfm/nber_w12329.pdf?abstractid=912439&mirid=5&type=2

The Effect of Maternal Education on Fertility and Infant Health: Evidence from School Entry Policies Using Exact Date of Birth

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

Tackling Climate Change with Machine Learning

This paper investigates the impact of the European Union Emissions Trading System (EU ETS) on technological change, exploiting installations-level inclusion criteria to estimate the System's causal impact on firms' patenting. We find that the EU ETS has increased low-carbon innovation among regulated firms by as much as 10%, while not crowding out patenting for other technologies. We also find evidence that the EU ETS has not impacted patenting beyond the set of regulated companies. These results imply that the EU ETS accounts for nearly a 1% increase in European low-carbon patenting compared to a counterfactual scenario.

/pdf/environmental-policy-and-directed-technological-change-3as0fmdr4n.pdf

Environmental Policy and Directed Technological Change: Evidence from the European carbon market

The European Union's Emissions Trading Scheme (EU ETS) is the world's largest market for carbon and the most significant multinational initiative ever taken to mobilize markets to protect the environment. It will be an important influence on the development and implementation of trading schemes in the US, Japan, and elsewhere. However, as is true of any pioneering public policy experiment, this scheme has generated much controversy. Pricing Carbon provides the first detailed description and analysis of the EU ETS, focusing on the first 'trial' period of the scheme (2005–7). Written by an international team of experts, it allows readers to get behind the headlines and come to a better understanding of what was done and what happened based on a dispassionate, empirically based review of the evidence. This book should be read by anyone who wants to know what happens when emissions are capped, traded, and priced.

Emilie Alberola

Papers

Pricing Carbon: The European Union Emissions Trading Scheme

Pricing Carbon: List of appendices

Pricing Carbon: Preface

Pricing Carbon: Linkage and global implications

Pricing Carbon: Emissions abatement