The PRIMAP-hist national historical emissions time series

To assess the history of greenhouse gas emissions and individual countries' contributions to emissions and climate change, detailed historical data are needed. We combine several published datasets to create a comprehensive set of emissions pathways for each country and Kyoto gas, covering the years 1850 to 2014 with yearly values, for all UNFCCC member states and most non-UNFCCC territories. The sectoral resolution is that of the main IPCC 1996 categories. Additional time series of CO2 are available for energy and industry subsectors. Country-resolved data are combined from different sources and supplemented using year-to-year growth rates from regionally resolved sources and numerical extrapolations to complete the dataset. Regional deforestation emissions are downscaled to country level using estimates of the deforested area obtained from potential vegetation and simulations of agricultural land. In this paper, we discuss the data sources and methods used and present the resulting dataset, including its limitations and uncertainties. The dataset is available from doi:10.5880/PIK.2016.003 and can be viewed on the website accompanying this paper (http://www.pik-potsdam.de/primap-live/primap-hist/)

Reduced Complexity Model Intercomparison Project Phase 1: introduction and evaluation of global-mean temperature response

Reduced-complexity climate models (RCMs) are critical in the policy and decision making space, and are directly used within multiple Intergovernmental Panel on Climate Change (IPCC) reports to complement the results of more comprehensive Earth system models. To date, evaluation of RCMs has been limited to a few independent studies. Here we introduce a systematic evaluation of RCMs in the form of the Reduced Complexity Model Intercomparison Project (RCMIP). We expect RCMIP will extend over multiple phases, with Phase 1 being the first. In Phase 1, we focus on the RCMs' global-mean temperature responses, comparing them to observations, exploring the extent to which they emulate more complex models and considering how the relationship between temperature and cumulative emissions of CO2 varies across the RCMs. Our work uses experiments which mirror those found in the Coupled Model Intercomparison Project (CMIP), which focuses on complex Earth system and atmosphere–ocean general circulation models. Using both scenario-based and idealised experiments, we examine RCMs' global-mean temperature response under a range of forcings. We find that the RCMs can all reproduce the approximately 1 ∘C of warming since pre-industrial times, with varying representations of natural variability, volcanic eruptions and aerosols. We also find that RCMs can emulate the global-mean temperature response of CMIP models to within a root-mean-square error of 0.2 ∘C over a range of experiments. Furthermore, we find that, for the Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP)-based scenario pairs that share the same IPCC Fifth Assessment Report (AR5)-consistent stratospheric-adjusted radiative forcing, the RCMs indicate higher effective radiative forcings for the SSP-based scenarios and correspondingly higher temperatures when run with the same climate settings. In our idealised setup of RCMs with a climate sensitivity of 3 ∘C, the difference for the ssp585–rcp85 pair by 2100 is around 0.23∘C(±0.12 ∘C) due to a difference in effective radiative forcings between the two scenarios. Phase 1 demonstrates the utility of RCMIP's open-source infrastructure, paving the way for further phases of RCMIP to build on the research presented here and deepen our understanding of RCMs

PRICE SEARCH IN PRODUCT MARKETS

This thesis utilizes a rich data source on the purchases of grocery products by a large panel of households in a single city to provide new empirical support for predictions from the theory of search for price information. By using the theory of order statistics, Stigler showed that the expected gain from search (in terms of the savings from finding lower prices) is positive and decreases with the amount of search. In sequential search models, in which search is terminated as soon as a price is found at or below a reservation price, the same relationship is shown to hold for the expected amount of search. Existing models assume a cost of search for each item. In the search for grocery products, however, the prices of many items can be found jointly once the costs of a trip to the store have been incurred. This is called joint search. When a consumer\u27s utility is maximized subject to the constraint of joint search, the relationship between the cost of the entire bundle of items and the expected amount of search is still the same as in the one-commodity case, but the price paid for individual items and the amount of search may have a much weaker association. These propositions are tested by regressing an index of the relative price paid on various functional forms of an index of the amount of search for grocery products. The regressions support the predictions that there are positive and diminishing returns to search in this market. In addition, the increase in the R(\u272) when storable and non-storable goods are combined support the prediction that the relationship between an index of prices paid and the amount of search is stronger when the index is for all goods subject to joint search than for subsets of items. The theory of the consumer trying to maximize utility, subject to both an income constraint and a time constraint on work, search and leisure, points to the simultaneous determination of quantities purchased, prices paid, and the amount of search. An empirical investigation of the influences on the amount of search should therefore utilize a simultaneous-equations model instead of the single-equation models used in the few other studies that exist. Furthermore, by relating the effects of exogenous influences on the amount of search to their influences on the quantities purchased, one can show that search for grocery products may be an increasing function of the wage rate for low-wage households, reach a maximum, and begin to decrease for still higher wages. The coefficients estimated by two-stage least squares have the predicted signs and many are statistically significant. Lower prices are shown to be associated with larger purchases. Those who make larger purchases tend to search more and hence find lower prices. In addition, single people with less time available tend to search less; the older and better educated people, who are presumably more experienced or more efficient in their use of time, search slightly more; and there is an indication of first rising and then falling amount of search as income rises, with maximum search apparently occurring above the median income level. No other study to which this one has been compared comes close to having so many significant variables associated with the amount of search by consumers in a product market

pyhector

Pyhector is a Python interface for the simple global climate carbon-cycle model Hector. Pyhector makes the simple climate model Hector easily installable and usable from Python and can for example be used in the analysis of mitigation scenarios, in integrated assessment models, complex climate model emulation, and uncertainty analyses. Source: https://github.com/openclimatedata/pyhecto

Using high-resolution imagery and deep learning to classify land-use following deforestation: a case study in Ethiopia

National-scale assessments of post-deforestation land-use are crucial for decreasing deforestation and forest degradation-related emissions. In this research, we assess the potential of different satellite data modalities (single-date, multi-date, multi-resolution, and an ensemble of multi-sensor images) for classifying land-use following deforestation in Ethiopia using the U-Net deep neural network architecture enhanced with attention. We performed the analysis on satellite image data retrieved across Ethiopia from freely available Landsat-8, Sentinel-2 and Planet-NICFI satellite data. The experiments aimed at an analysis of (a) single-date images from individual sensors to account for the differences in spatial resolution between image sensors in detecting land-uses, (b) ensembles of multiple images from different sensors (Planet-NICFI/Sentinel-2/Landsat-8) with different spatial resolutions, (c) the use of multi-date data to account for the contribution of temporal information in detecting land-uses, and, finally, (d) the identification of regional differences in terms of land-use following deforestation in Ethiopia. We hypothesize that choosing the right satellite imagery (sensor) type is crucial for the task. Based on a comprehensive visually interpreted reference dataset of 11 types of post-deforestation land-uses, we find that either detailed spatial patterns (single-date Planet-NICFI) or detailed temporal patterns (multi-date Sentinel-2, Landsat-8) are required for identifying land-use following deforestation, while medium-resolution single-date imagery is not sufficient to achieve high classification accuracy. We also find that adding soft-attention to the standard U-Net improved the classification accuracy, especially for small-scale land-uses. The models and products presented in this work can be used as a powerful data resource for governmental and forest monitoring agencies to design and monitor deforestation mitigation measures and data-driven land-use policy

IndEcol/country_converter: ioc and continent classification

<h2>1.2 - 20231212</h2> <h3>Classifications</h3> <ul> <li>added IOC classification (by @Azrael3000)</li> <li>added 7 continents classification (by @marthhoi)</li> <li>assigned Heard Island and McDonald Islands to Antarctica</li> </ul&gt