Global long-term greenhouse gas mitigation emission scenarios based on AIM

2000 ◽  
Vol 3 (2) ◽  
pp. 239-254 ◽  
Author(s):  
Kejun Jiang ◽  
Tsuneyuki Morita ◽  
Toshihiko Masui ◽  
Yuzuru Matsuoka
Keyword(s):  
Greenhouse Gas ◽  
Greenhouse Gas Mitigation ◽  
Emission Scenarios

18. Climate Change Law

2019 ◽  
pp. 599-639
Author(s):  
Elizabeth Fisher ◽  
Bettina Lange ◽  
Eloise Scotford
Keyword(s):  
Climate Change ◽  
Greenhouse Gas ◽  
Greenhouse Gas Mitigation ◽  
Eu Member States ◽  
Mitigation And Adaptation ◽  
Law And Policy ◽  
Trading Scheme ◽  
The Uk ◽  
The Eu

This chapter examines the fast-moving area of law relating to climate change. This includes a considerable body of public international law, from the UN Framework Convention on Climate Change to the legally innovative Paris Agreement 2015. The chapter also considers legal developments at the EU and UK levels, which both contain a rich body of climate law and policy. The EU and the UK are both seen as ‘world leaders’ in climate law and policy. In EU law, this is due to the EU greenhouse gas emissions trading scheme and the EU’s leadership in advocating ambitious greenhouse gas mitigation targets and in implementing these targets flexibly across the EU Member States through a range of regulatory mechanisms. The UK introduced path-breaking climate legislation in the Climate Change Act 2008, which provided an inspiring model of climate governance, legally entrenching long-term planning for both mitigation and adaptation. The chapter concludes with an exploration of climate litigation, a new and growing field of inquiry.

scholarly journals Long-Term Field Data and Climate-Habitat Models Show That Orangutan Persistence Depends on Effective Forest Management and Greenhouse Gas Mitigation

PLoS ONE ◽  
2012 ◽  
Vol 7 (9) ◽  
pp. e43846 ◽  
Author(s):  
Stephen D. Gregory ◽  
Barry W. Brook ◽  
Benoît Goossens ◽  
Marc Ancrenaz ◽  
Raymond Alfred ◽  
...  
Keyword(s):  
Forest Management ◽  
Greenhouse Gas ◽  
Field Data ◽  
Greenhouse Gas Mitigation ◽  
Habitat Models ◽  
Term Field

scholarly journals The Contribution of Non-CO2 Greenhouse Gas Mitigation to Achieving Long-Term Temperature Goals

Energies ◽  
2017 ◽  
Vol 10 (5) ◽  
pp. 602 ◽  
Author(s):  
Ajay Gambhir ◽  
Tamaryn Napp ◽  
Adam Hawkes ◽  
Lena Höglund-Isaksson ◽  
Wilfried Winiwarter ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Greenhouse Gas Mitigation

scholarly journals Future Predictions of Rainfall and Temperature Using GCM and ANN for Arid Regions: A Case Study for the Qassim Region, Saudi Arabia

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1260 ◽  
Author(s):  
Khalid Alotaibi ◽  
Abdul Ghumman ◽  
Husnain Haider ◽  
Yousry Ghazaw ◽  
Md. Shafiquzzaman
Keyword(s):  
Saudi Arabia ◽  
Greenhouse Gas ◽  
Greenhouse Gas Emission ◽  
Data Driven ◽  
Emission Scenarios ◽  
Gas Emission ◽  
The Future ◽  
Emissions Scenarios ◽  
The Impact

Future predictions of rainfall patterns in water-scarce regions are highly important for effective water resource management. Global circulation models (GCMs) are commonly used to make such predictions, but these models are highly complex and expensive. Furthermore, their results are associated with uncertainties and variations for different GCMs for various greenhouse gas emission scenarios. Data-driven models including artificial neural networks (ANNs) and adaptive neuro fuzzy inference systems (ANFISs) can be used to predict long-term future changes in rainfall and temperature, which is a challenging task and has limitations including the impact of greenhouse gas emission scenarios. Therefore, in this research, results from various GCMs and data-driven models were investigated to study the changes in temperature and rainfall of the Qassim region in Saudi Arabia. Thirty years of monthly climatic data were used for trend analysis using Mann–Kendall test and simulating the changes in temperature and rainfall using three GCMs (namely, HADCM3, INCM3, and MPEH5) for the A1B, A2, and B1 emissions scenarios as well as two data-driven models (ANN: feed-forward-multilayer, perceptron and ANFIS) without the impact of any emissions scenario. The results of the GCM were downscaled for the Qassim region using the Long Ashton Research Station’s Weather Generator 5.5. The coefficient of determination (R2) and Akaike’s information criterion (AIC) were used to compare the performance of the models. Results showed that the ANNs could outperform the ANFIS for predicting long-term future temperature and rainfall with acceptable accuracy. All nine GCM predictions (three models with three emissions scenarios) differed significantly from one another. Overall, the future predictions showed that the temperatures of the Qassim region will increase with a specified pattern from 2011 to 2099, whereas the changes in rainfall will differ over various spans of the future.

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