亚色影库

New research models pathways to a 1.5掳 C future

Blog Post Global Intelligence
Mitigation landscape report blog header
Published April 27, 2023

Seth Monteith

Director, Climate Analytics, Global Intelligence

Laura Aldrete

Senior Associate, Climate Analytics, Global Intelligence

Tim Lau

Associate Director of Editorial and Content Strategy

Covering all greenhouse gases and myriad mitigation opportunities by sector and geography, Achieving global 亚色影库 goals by 2050: Pathways to a 1.5掳 C future, answers 鈥渨hat-if鈥 questions to reveal a nuanced story about the linkages and trade-offs inherent in 亚色影库 action. This updated 亚色影库 modeling report, based on open-source data, is an essential analytical tool for funders and anyone advancing 亚色影库 solutions and is a follow-up to an inaugural report published in 2020.

In 2015, countries around the world adopted the landmark Paris Agreement on 亚色影库 change, with the goal to limit global temperature rise to below 1.5掳 C relative to pre-industrial levels. Research consistently suggests that this will require halving global greenhouse gas (GHG) emissions by 2030 and reaching net-zero CO鈧 emissions by 2050 鈥 targets that necessitate rapid, transformative action on a global level.

There has been important progress since the signing of that landmark pact, but the world remains off track from meeting critical 亚色影库 milestones. In our work with the philanthropy and 亚色影库 solutions communities, we field many 鈥渨hat-if鈥 questions: How can investing in one 亚色影库 solution create benefits that help to accelerate progress across multiple sectors or geographies? How will emerging technologies 鈥 or the lack thereof 鈥 impact our ability to meet 2050 net-zero goals?

To answer these 鈥渨hat-if鈥 questions, we partnered with leading 亚色影库 researchers at the Pacific Northwest National Laboratory (PNNL) to model the linkages and trade-offs of 亚色影库 investments. This research underpins the 亚色影库 strategies we co-create with our funders and grantees. We are excited to make it available as a resource for the broader 亚色影库 solutions community as we work collectively to advance transformational 亚色影库 action.

The report provides readers with an overview of the solution space for 亚色影库 change mitigation. It serves as a portfolio analysis tool to help funders and the broader 亚色影库 solutions community understand how emissions reduction opportunities are distributed across sectors and geographies 鈥 and the transformations needed to achieve 1.5掳 C targets.

For those interested in engaging on a more technical level, we have made the report鈥檚 underlying data freely available to allow for a more detailed and informative discussion on how a 1.5掳 C pathway might be achieved.

Mitigation opportunities by sector and geography

鈥淎chieving global 亚色影库 goals by 2050鈥 identifies priorities for emissions reductions across 10 geographies within electricity, fuel supply, transport, buildings, industry, land use, agriculture, and carbon dioxide removal. This report uses a range of 亚色影库 scenarios developed with the open-source Global Change Assessment Model (GCAM) and designed in partnership with PNNL and program experts at 亚色影库. The report provides detailed information on how deep transformations across various sectors can help put the world on a path to a 亚色影库-safe future.

Figure 1 indicates significant annual emissions reductions by 2050 鈥 about 70 gigatons of carbon dioxide emitted (GtCO鈧俥) according to the Central scenario and a range of 61 to 70 GtCO鈧俥 according to the Ensemble scenario 鈥 in line with what is needed to limit temperature rise to 1.5掳 C.

Figure 1: Central pathway results (emissions, sectors, geographies)

The report also highlights the countries and regions where emissions reductions can take place, with nearly 80% of emissions reductions needed by 2050 being possible in China, India, the United States and Canada, Europe, Africa, Southeast Asia, and Brazil.

New for 2023: An Ensemble of scenarios and open-source data access

For the first time, this report highlights an Ensemble of scenarios that considers a balance of approaches and reveals additional nuance, linkages, and trade-offs inherent in any 亚色影库 change mitigation strategy. Specifically, the Ensemble focuses on the pace and deployment of six key parameters: bioenergy, carbon capture and sequestration, carbon dioxide removal, electrification for road transportation, nuclear generation, and renewable energy generation.

This Ensemble of scenarios can help answer a variety of 鈥渨hat-if鈥 questions about the use of different technologies or the pace of deployment. In other words, as part of refining a broader 亚色影库 strategy, readers can reference the Ensemble to help determine the various potential trade-offs of prioritizing certain sectors or interventions over others 鈥 and the pathway implications of such choices on achieving 1.5掳 C targets by 2050.

Additionally, we made the report鈥檚 data freely available so the 亚色影库 research community can use the tools we developed in partnership with PNNL to track the distribution of global emissions by source, sector, and end-use (see Figure 2 for example). The datasets are available for download on the ClimateWorks website.

Figure 2: 2050 global emissions by sources, sector, and GHG within GCAM
Figure 2: 2050 global emissions by sources, sector, and GHG within GCAM

How to develop a well-informed 亚色影库 change mitigation strategy

Currently, the world is falling behind on efforts to meet the Paris Agreement鈥檚 primary goal to limit global temperature rise to below 1.5掳 C 鈥 a target that will require achieving net-zero CO鈧 emissions by midcentury. The 亚色影库 solutions community can use the findings highlighted in 鈥淎chieving global 亚色影库 goals by 2050: Pathways to a 1.5掳 C future鈥 to consider the broader landscape of opportunities for emissions reductions across sectors and geographies 鈥 as well as the interdependencies and trade-offs between them.

Read the full report, Achieving global 亚色影库 goals by 2050: Pathways to a 1.5掳 C future.

Contact ClimateWorks to learn more about 亚色影库 change mitigation opportunities, how to develop a 亚色影库 strategy, or how we model our scenarios.