Climate Policy

Abstract: We present a self-consistent, large ensemble, high-resolution global dataset of long-term future climate, which accounts for the uncertainty in climate system response to anthropogenic emissions of greenhouse gases and in geographical patterns of climate change. The dataset is developed by applying an integrated spatial disaggregation (SD) - bias-correction (BC) method to climate projections from the MIT Integrated Global System Modeling (IGSM) framework. Four emissions scenarios are considered that represent energy and environmental policies and commitments of potential future pathways, namely, Reference, Paris Forever, Paris 2°C and Paris 1.5°C. The dataset contains nine key meteorological variables on a monthly scale from 2021 to 2100 at a spatial resolution of 0.5°x 0.5°, including precipitation, air temperature (mean, minimum and maximum), near-surface wind speed, shortwave and longwave radiation, specific humidity, and relative humidity.

We demonstrate the dataset’s ability to represent climate-change responses across various regions of the globe.

This dataset can be used to support regional-scale climate-related impact assessments of risk across different applications that include hydropower, water resources, ecosystem, agriculture, and sustainable development.

Emissions of CFC-11, a chlorofluorocarbon once frequently used in cooling and insulation systems to improve the quality of life, can also endanger life. Upon entry into the stratosphere where solar ultraviolet radiation is strong, CFC-11 decomposes, resulting in the release of chlorine, which degrades the ozone layer that shields life from harmful UV rays. In 2018, a team of scientists discovered an alarming upward spike in global CFC-11 emissions from 2013 to 2017.

Abstract: Organizational decisions to mitigate climate change are often focused solely on reducing greenhouse gas emissions, but also can have multiple sustainability-related impacts. A substantial area of impact from reducing greenhouse gases relates to air quality, where reductions in fossil fuel use can cause health damages locally and regionally. While much research has quantified the air quality benefits of large-scale strategies to reduce greenhouse gas emissions, information about the different impacts of organizational Scope 1–3 emissions on air quality is lacking. We use data from two universities and one multinational corporation based in the northeast U.S. to examine the magnitude and location of air quality changes associated with reducing carbon emissions under two different strategies: replacing purchased fossil-based electricity with renewable energy (scope 2), and reducing personnel business travel by air (scope 3). We estimate the marginal climate response and spatially-resolved air quality impacts associated with these two strategies. To do this, we first use an energy system model (US Energy Grid Optimization, US-EGO) to simulate electricity grid responses and related emissions (CO2, NOx and SO2) due to organizational electricity consumption. We calculate business travel emissions (principally NOx, SOx and non-volatile particulate matter) with the Aviation Emission Inventory Code (AEIC) based on detailed flight data provided by the organizations. For both sectors, we run GEOS-Chem High Performance (GCHP), an atmospheric chemistry-transport model, to simulate the ground-level concentration of ozone and fine particulate matter (PM2.5). We estimate the health damages from these two pollutants with Concentration Response Functions (CRFs). We calculate the social costs using the Social Cost of Carbon (SCC) for carbon emissions, and use a Value of Statistical Life (VSL) to quantify costs for air-pollution-induced health damages. We explore how marginal estimates of damages vary depending on system-level assumptions. Finally, we compare our estimates from detailed modeling with results from reduced form air quality estimators (InMAP, AP2 and EASIUR) to identify how these tools might assist organizations in prioritizing emissions reductions to maximize overall air quality benefits.

Abstract 

The ability to rapidly simulate the local climate implications of a large number of future climate policy scenarios is important for planning and implementing adequate climate mitigation and adaptation policies. Given the societal impacts from rising local temperature, we build an emulator of spatially resolved near-surface air temperature responses to carbon dioxide (CO2) emissions. Near surface air temperature is approximately proportional to cumulative carbon dioxide (CO2) emissions, allowing for linearization of the temperature response to emissions scenarios. This linearity enables us to diagnose Green’s Functions for the spatial temperature response to CO2 emissions from pulse simulations conducted as part of the CDRMIP experiments. We then apply this emulator across a wide range emissions scenarios to estimate local temperature responses.

We evaluate this emulator with two CMIP6 experiments: 1) a 1% increase in CO2 concentration, and 2) an experiment that branches from this after concentrations of 1000 PgC are reached. We find that this emulation approach captures the spatial temperature response to CO2 emissions within one standard deviation of the CMIP6 range, with some limited accuracy in polar regions where nonlinearities in climate feedbacks dominate and internal variability may influence the Green’s Function. This approach incorporates emissions path dependency, accounting for various timescales of warming due to CO2 emissions. It is useful for evaluating large ensembles of policy scenarios that are otherwise prohibitively expensive to simulate using earth system models, as it takes less than one second to emulate 90 years of temperature response. We apply this emulator to quantify differing local temperature responses when a global mean of 2ºC is reached, showing that some locations (such as Lagos and Buenos Aires) warm slower than the global mean, while others warm faster (such as Boston and Shanghai). We also evaluate varying CO2 emissions trajectories with the same cumulative emissions, showing that the resulting temperature changes are path dependent.

Plain-language Summary

The ability to quantify the climate impacts of changes in future emissions due to various climate policies is important for planning and implementing adequate climate mitigation and adaptation. Given the societal impacts from rising local temperature, we build a rapid model that can quantify local temperature responses to changes in carbon dioxide (CO2) emissions, a key greenhouse gas responsible for climate change. This simple approach takes advantage of a proportional relationship between total CO2 emissions and temperature, and it takes only one second to quantify temperature impacts over 90 years. We test this approach against the Climate Model Intercomparison Project Phase 6 (CMIP6) experiments, finding some limited accuracy in polar regions. We then use this approach to quantify the local temperature impacts in different cities when a global mean increase of 2 ºC is reached, showing that some cities warm faster than the global mean and others warm slower. We also show that the emissions pathway matters, even if the same total CO2 emissions are reached. Importantly, this approach can be used to estimate local temperature response to dozens of policy scenarios without the computational power and time needed for running a climate model.

Abstract: Energy-economic and coupled human-natural system models are often used to explore potential energy futures and their implications for climate. There are many uncertain assumptions in the human system models that drive those futures, and in previous work we used a traditional Monte Carlo approach to explore socio-economic uncertainties in a multi-sector, multi-region energy-economic-emissions model of the global economy and generate probabilistic ensembles. The amount of data and information generated from these large ensembles is immense and it can be difficult to sort through and extract relevant insights. The goal of this work is to apply a variety of scenario discovery techniques to the probabilistic ensembles in order to extract insights related to energy futures, with a particular focus on the penetration of renewable energy. We apply Classification and Regression Trees (CART) with Random Forest Classifier (RFC) and Time Series Clustering (TSC) to explore key input drivers of the share of renewable generation, how those drivers can vary over time and across regions, different types of pathways for renewables, and relationships among model outputs. We find that the key drivers of renewables can vary significantly based on the policy scenario, region and time period. In particular, the time series clustering revealed interesting dynamics that are missed by looking at individual years. Through this work we demonstrate the value of scenario discovery techniques in drawing insights from large ensembles of energy-projecting models by facilitating the identification of drivers, relationships among variables and areas of the uncertainty space that are particularly interesting or relevant.

Abstract: Negative carbon emissions options are required to meet long-term climate goals in many countries. One way to incentivize these options is by paying farmers for carbon sequestered by forests through an emissions trading scheme (ETS). New Zealand has a comprehensive ETS, which includes incentives for farmers to plant permanent exotic forests.

This research uses an economy-wide model, a forestry model and land use change functions to measure the expected proportion of farmers with trees at harvesting age that will change land use from production to permanent forests in New Zealand from 2014 to 2050. We also estimate the impacts on carbon sequestration, the carbon price, gross emissions, GDP and welfare.

When there is forestry land use change, the results indicate that the responsiveness of land owners to the carbon price has a measured impact on carbon sequestration. For example, under the fastest land use change scenario, carbon sequestration reaches 29.93 Mt CO2e by 2050 compared to 23.41 Mt CO2e in the no land use change scenario (a 28% increase). Even under the slowest land use change scenario, carbon sequestration is 25.89 Mt CO2e by 2050 (an 11% increase compared with no land use change). This is because, if foresters decide not to switch to permanent forests in 1 year, carbon prices and ultimately incentives to convert to permanent forests will be higher in future years.

Part of this research was completed while co-author Dominic White was visiting the MIT Joint Program.

Authors' Impact/Purpose: This report documents methods used to analyze the economic and environmental impacts of the Inflation Reduction Act (IRA), enacted in August 2022 by the United States Congress. The analysis relies on the U.S. Regional Energy Policy (USREP) economy-wide model developed by researchers at the Massachusetts Institute of Technology (MIT), linked with the Regional Energy Deployment System (ReEDS) electricity sector model developed by researchers at the National Renewable Energy Laboratory (NREL).

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