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Abstract. Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We evaluate 136 statistical models trained on a precipitation dataset with five landslide-triggering precipitation events recorded near Sitka, Alaska, USA, as well as > 6000 d of non-triggering rainfall (2002–2020). We also conduct extensive statistical evaluation for three primary purposes: (1) to select the best-fitting models, (2) to evaluate performance of the preferred models, and (3) to select and evaluate warning thresholds. We use Akaike, Bayesian, and leave-one-out information criteria to compare the 136 models, which are trained on different cumulative precipitation variables at time intervals ranging from 1 h to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as by testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without slide activity. Our statistical analyses show that 3 h precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale of precipitation combined with the limited role of antecedent conditions likely reflects the rapid draining of porous colluvial soils on the very steep hillslopes around Sitka. Although frequentist and Bayesian inferences produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. We use the resulting estimates of daily landslide probability to establish two decision boundaries that define three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning "dashboard" system (https://sitkalandslide.org/, last access: 9 October 2023). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers.

The study analyses the country background, emissions trends, ongoing activities and barriers relating to the implementation of the Nationally Determined Contribution (NDC) of Colombia under the ⁠UNFCCC⁠. A special emphasis is laid on further mitigation potentials in the fields of renewable energy production from wind and solar PV, social housing, forest conservation in existing illicit crop substitution programmes and cattle. A chapter is dedicated to coal export and use.

The study analyses the country background, emissions trends, ongoing activities and barriers relating to the implementation of the Nationally Determined Contribution (NDC) of Ethiopia under the ⁠UNFCCC⁠. A special emphasis is laid on further mitigation potentials in the fields of agriculture, forestry and low-emission transport.

The study analyses the country background, emissions trends, ongoing activities and barriers relating to the implementation of the Nationally Determined Contribution (NDC) of the Republic of Marshall Islands under the ⁠UNFCCC⁠. A special emphasis is laid on further mitigation potentials in the fields of transport – especially low-carbon domestic shipping – and waste reduction, disposal and processing.

This report is part of the "Implementation of Nationally Determined Contributions" (NDCs) project (FKZ 3716 4111 80), which considers NDC implementation in 10 countries: Colombia, Ethiopia, Georgia, Indonesia, Iran, Kenya, Marshall Islands, Morocco, Peru, and Viet Nam. This project places a special emphasis on identifying potential barriers to NDC implementation and mitigation potentials which could go beyond the current NDCs. The country reports analyse the NDCs in terms of their robustness and coherence with other national or sectoral plans and targets, and put them into the context of additional mitigation potentials and other national circumstances. For countries where coal plays a critical role in consumption or national production, the analysis covers further details on this sector, including the economic relevance and local impacts of coal production or consumption. The content is based on available literature from research and public sector information on policies and institutions. This report summarises the findings on coal given in the country reports of Colombia, Indonesia and Viet Nam. It has been augmented by a literature analysis on global coal trends and structural change in coal mining areas as well as very recent country specific publications which became available only after the finalisation of the three country reports. The project was suggested and is financed by the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety, supervised by the German Environment Agency and carried out by independent think tanks - NewClimate Institute and Wuppertal Institute. The country reports are a continuation of similar previous efforts (project numbers 3713 41 102, 3711 41 120, 360 16 022, 364 01 003 and 363 01 128) and aim to inform policy makers and the interested public about the implementation of NDCs in individual countries. The choice of countries is based on developing countries with which Germany works closely on climate change topics. The country reports are scientific in nature, and all suggestions are derived by the authors from careful analysis, having in mind the individual backgrounds of countries. They aim to increase knowledge about implementation of mitigation potentials to meet the globally agreed goal of staying within a temperature increase of 1.5°C or well below 2°C above preindustrial levels, without intending to prescribe specific policies.

Climate policy needs to account for political and social acceptance. Current national climate policy plans proposed under the Paris Agreement lead to higher emissions until 2030 than cost-effective pathways towards the Agreements' long-term temperature goals would imply. Therefore, the current plans would require highly disruptive changes, prohibitive transition speeds, and large long-term deployment of risky mitigation measures for achieving the agreement's temperature goals after 2030. Since the prospects of introducing the cost-effective policy instrument, a global comprehensive carbon price in the near-term, are negligible, we study how a strengthening of existing plans by a global roll-out of regional policies can ease the implementation challenge of reaching the Paris temperature goals. The regional policies comprise a bundle of regulatory policies in energy supply, transport, buildings, industry, and land use and moderate, regionally differentiated carbon pricing. We find that a global roll-out of these policies could reduce global CO2 emissions by an additional 10 GtCO2eq in 2030 compared to current plans. It would lead to emissions pathways close to the levels of cost-effective likely below 2 °C scenarios until 2030, thereby reducing implementation challenges post 2030. Even though a gradual phase-in of a portfolio of regulatory policies might be less disruptive than immediate cost-effective carbon pricing, it would perform worse in other dimensions. In particular, it leads to higher economic impacts that could become major obstacles in the long-term. Hence, such policy packages should not be viewed as alternatives to carbon pricing, but rather as complements that provide entry points to achieve the Paris climate goals.