We study the optimal recycling of carbon tax revenue in a Mirrleesian economy. Our starting point is a tax schedule that optimally trades-off equity and efficiency (for some welfare weights). If then the government has to implement a carbon tax (e.g. because of the Paris agreement or because suddenly it becomes aware of climate change), what should the government do with the additional tax revenue? We characterize reforms of the nonlinear labor income taxes that fully compensate for the individual utility losses from carbon taxes, neutralize the negative labor supply incentives due to Carbon taxes and are budget neutral. We label these reforms as compensating tax reforms. If Engel curves are linear, these tax reforms are also the optimal response. If Engel curves are nonlinear, they are no longer optimal because the fact that households differ in how easy they can substitute carbon consumption, affects the governments preference for redistribution.

We develop a quantitative growth model with directed technical change to study how material use and recycling rates respond to resource scarcity. In a static economy, scarcity naturally induces greater circularity, i.e. lower material shares and higher recycling rates. On the balanced growth path, circularity depends critically on technology parameters and policy. Indeed, short- and long-run policy implications can be quite different: taxes on virgin material extraction raise recycling in the short run and lower it in the long run. We quantify the model by combining data from U.S. material flow accounts, production (NIPA) accounts and U.S. Environmental Protection Agency (EPA) data on waste generation and recycling. Absent further policy, the recycling rate will peak at 48% around 2060 and then decline. Achieving the EPA target of 50% recycling rate by 2030 would require a 50% tax on virgin material use or a subsidy covering 66% of recycling costs.

Formulating and implementing climate policy requires a detailed understanding of likely pathways of future carbon emissions. However, most existing tools to provide forecasts for such emissions are limited. Projections are predominantly made for the long-term, lacking the necessary detail within the typical policy horizon of one to five years. Most short-term forecasting models are closed-source ‘black-boxes’ that do not reveal in detail how the obtained forecasts are generated. Here we develop a generalised framework for modelling climate and environmental policies within an empirical macroeconometric modelling approach. The resulting open source empirical macroeconomic (‘OSEM’) model is an econometric model builder that provides a platform to generate empirical forecasts of sectoral carbon emissions as well as the wider macroeconomy. The underlying estimated models are based on dynamic time series regressions matching a structural VAR allowing for model selection, outliers, insample structural breaks, and automatic forecast evaluation. We present an illustrative example providing

the first short run sectoral greenhouse gas emission forecasts for Austria. The resulting model builder is open source, easily portable across countries, can be updated in real time when new data is released, and aims to improve transparency and flexibility in policy forecasts.