The majority of U.S. adults believe that non-government scientists and educators are the most trustworthy sources for information about climate change, with **** percent of respondents in 2022. By comparison, nearly ** percent of respondents said they considered environmental groups trustworthy, and some ** percent said they considered college professors/educators trustworthy.

According to a survey conducted on climate change in Japan in September 2023, adding up to around 92.4 percent, the majority of respondents stated that they learned about the risks of global warming on TV or radio. Around 59 percent of respondents mentioned newspapers, magazines, or books as a source of climate change impacts.

According to an ********** survey on climate change conducted in the United States, approximately ** percent of the respondents claimed they heard about global warming in the media at least once a week. Just ***** percent of respondents stated that they had never heard about global warming in the media.

(1) This is the dataset simulated by high resolution atmospheric model of which horizontal resolution is 60km-mesh over the globe (GCM), and 20km over Japan and surroundings (RCM), respetively. The climate of the latter half of the 20th century is simulated for 6000 years (3000 years for the Japan area), and the climates 1.5 K (*2), 2 K (*1) and 4 K warmer than the pre-industrial climate are simulated for 1566, 3240 and 5400 years, respectivley, to see the effect of global warming. (2) Huge number of ensembles enable not only with statistics but also with high accuracy to estimate the future change of extreme events such as typoons and localized torrential downpours. In addtion, this dataset provides the highly reliable information on the impact of natural disasters due to climate change on future societies. (3) This dataset provides the climate projections which adaptations against global warming are based on in various fields, for example, disaster prevention, urban planning, environmetal protection, and so on. It would realize the global warming adaptations consistent not only among issues but also among regions. (4) Total size of this dataset is 3 PB (3 x the 15th power of 10 bytes).

(*1) Datasets of the climates 2K warmer than the pre-industorial climate is available on 10th August, 2018. (*2) Datasets of the climates 1.5K warmer than the pre-industorial climate is available on 8th February, 2022.

Introduction

Rainfall Statistics: Floods and extreme rainfall are the leading causes of natural disasters around the world. In recent years, due to global warming, such events have significantly increased, leading to thousands of innocent lives and economic losses. Rainfall is important in terms of economic, social, scientific, and cultural development across the world.

However, in recent years, rainfalls have become irregular; they either fall in slight or extreme numbers. Although our technology helps us to understand the patterns, impacts, and mechanisms, the safety of people remains the biggest concern. Let’s take a look at Rainfall Statistics to understand worldwide insights.

In 2023, excavators emitted around 38 percent of nitrogen oxides among all engineering machinery vehicles in China. Vehicles were one of the main sources of air pollution in China.

The Energy Information Administration (EIA), the independent and statistical agency of the Department of Energy (DOE), provided estimates of U.S. emissions of the principal greenhouse gases - carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), halocarbons (CFCs, HCFCs), carbon monoxide (CO), nitrogen oxides (NOx), and non-methane volatile organic compounds (VOCs). The report of greenhouse gas emission was mandated by the U.S. Congress through section 1605(a) of the Energy Policy Act of 1992 (Title XVI).

 The report contains many tables and statistics regarding U.S.
 greenhouse gas emissions including: carbon emissions from energy
 consumption, energy production, industrial sources, and energy-related
 carbon emissions (such as from petroleum, coal and natural gas);
 methane emissions from energy production and use, combustion,
 landfills, agricultural sources (including domesticated and wild
 animals and wetland rice cultivation); nitrous oxide emissions from
 nitrogen fertilizer use, fossil fuel combustion, adipic acid
 production, nitric acid production, and waste sources; CFC production;
 criteria pollutants (carbon monoxide, nitrogen oxides, volatile
 organic compounds). Greenhouse gas emissions from land use changes
 were also estimated.

 The document includes an historical time series of energy-related
 U.S. carbon emissions from 1949-1999.

 The document can be obtained from the National Energy Information
 Center at 202-586-8800 or from the U.S. Government Printing Office at
 202-653-2050 or the Superintendent of Documents at 202-783-3238. Mail
 orders should be sent to:

 U.S. Government Printing Office
 P.O. Box 371954
 Pittsburgh, PA 15250-7954

 The most recent document can be obtained online at:
 "http://www.eia.doe.gov/oiaf/1605/ggrpt/index.html"

 For other environmental publications from DOE/EIA, see:
 "http://www.eia.doe.gov/environment.html"

The latest National Statistics on forestry produced by the Forestry Commission were released on 22 September 2016 according to the arrangements approved by the UK Statistics Authority.

Detailed statistics are published in the web publication Forestry Statistics 2016, with an extract in Forestry Facts & Figures 2016. They include UK statistics on woodland area, planting, timber, trade, climate change, environment, recreation, employment and finance & prices as well as some statistics on international forestry. Where possible, figures are also provided for England, Wales, Scotland and Northern Ireland.

This dataset covers statistics on carbon in forests, the Woodland Carbon Code and public attitudes to climate change. Attribution statement:

CO2 emissions fell by roughly one percent in 2024 in China. In the first quarter of 2025, CO2 emission even fell by 1.6 percent. According to estimates, China can reach peak emissions in 2025, despite increasing energy demand. This is possible due to investments in the construction of renewable energy infrastructure.

The Climate Change Mitigation in Agriculture Statistics publication brings together statistics on agriculture which track progress on greenhouse gas (GHG) performance. The publication summarises available evidence and interprets it in the context of GHGs. It also incorporates emerging statistics which inform understanding of GHGs in agriculture as research.

Source agency: Environment, Food and Rural Affairs

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: Greenhouse gases from agriculture

These data include the supplementary materials of the article Climate change skepticism of European farmers and implications for effective policy actions by Kröner et al. 2015. Tables 1-7 can be found in the word document. Table 1 includes Descriptive statistics of the overall samples. Supplementary Table 2 includes Predicted values on attribution skepticism (1-5) and impact skepticism (1-10) for non-farmers and farmers in model with multilevel structure with random intercept and random coefficient for farmer. Supplementary Table 3 includes Number of observations and mean attitude values per country for farmers and the remaining working population (non-farmers), and additional country factor used for the statistical analysis. Supplementary Table 4 includes Eurobarometer special issues climate change 2011-2021 multilevel models with impact skepticism as dependent variable. Supplementary Table 5 includes European social survey wave 8 and wave 10 multilevel models. Supplementary Table 6 includes Ranked index for national climate risks for agriculture in European countries based on Trnka et al. (2011) and Zhao et al., (2022). A higher score on this variable indicates a higher climate risk at the national level. Supplementary Table 7 includes comparison Share of agricultural employment per country and representation of farmers in ESS and Eurobarometer. The excel file with the supplementary data 1. include the calculation of climate risk score.

ICARIA project had as one of its main purposes to develop coherent, reliable and usable downscaled climate projections from the last CMIP6 in order to construct the basis for efficient support to climate adaptation and decision-making of the related stakeholders, supporting the adaptation of critical assets within the project. These projections were obtained with also the purpose to be freely available for further use in subsequent studies and, hence, foster adaptation to climate change in more areas. Therefore, ICARIA’s climate information is already based on CMIP6 models and incorporating in its workflow the current SSPs. The presented high-resolution future climate projections display a unique dataset. These models will provide the scenarios to be considered within the Risk Assessment and the design and development of all adaptation measures coming as ICARIA outcomes.

For further details, find here a brief of the methodology followed:

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The statistical downscaling methodology applied in ICARIA by FIC, named FICLIMA (Ribalaygua et al. 2013), consists of a two-step analogue/regression statistical method which has been used in national and international projects with good verification results (i.e.: Monjo et al. 2016). The first step is common for all simulated climate variables and it is based on an analogue stratification (Zorita et al. 1993). An analogue method was applied based on the hypothesis that ‘analogue’ atmospheric patterns (predictors) should cause analogue local effects (predictands), which means that the number of days that were most similar to the day to be downscaled was selected. The similarity between any two days was measured according to three nested synoptic windows (with different weights) and four large-scale fields using a pseudo-Euclidean distance between the large-scale fields used as predictors. For each predictor, the weighted Euclidean distance was calculated and standardised by substituting it with the closest percentile of a reference population of weighted Euclidean distances for that predictor. This method is a good method for reproducing nonlinear relationships between predictors and the predictands, but it could not be used to simulate values outside of the range of observed values. In order to overcome this problem and obtain a better simulation, a second step was required.

For this second step, the procedures applied depend on the variable of interest. To determine the temperature, multiple linear regression analysis for the selected number of most analogous days was performed for each station and for each problem day. From a group of potential predictors, the linear regression selected those with the highest correlation, using a forward and backward stepwise approach.

For precipitation, a group of m problem days (we use the whole days of a month) is downscaled. For each problem day we obtain a “preliminary precipitation amount” averaging the rain amount of its n most analogous days, so we can sort the m problem days from the highest to the lowest “preliminary precipitation amount”. For assigning the final precipitation amount, all amounts of the m×n analogous days are sorted and clustered in m groups. Every quantity is finally assigned, orderly, to the m days previously sorted by the “preliminary precipitation amount”.

For wind or relative humidity, the second step is a transfer function between the observed probability distribution and the simulated one using the averaged values from the n = 30 analogous days. Particularly, a parametric bias correction was performed to the time series obtained from the analogue stratification (first step). In order to estimate the improvement of this procedure, the bias correction was also applied to the direct model outputs.

This second step done at a daily scale with an inner thorough verification procedure is essential and the main differentiating process of FICLIMA method. It extends beyond mean values to include extremes and covers all time scales, including daily intervals. With the verification it can be proven If the method correctly simulates changes from one day to the next, indicating an effective capture of the underlying physical connections between predictors and predictands. These physical links remain relatively consistent, even in the face of climate change (as opposed to purely empirical relationships that might shift). In essence, this approach theoretically addresses the primary challenge in statistical downscaling known as the non-stationarity problem. This problem questions the stability of predictor/predictand relationships established in the past, probing whether these relationships will persist in the future.

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The dataset shared here includes information for the three case studies tackled in ICARIA: Barcelona Metropolitan Area (AMB), Salzburg Region (SLZ), and South Aegean Region (SAR). The information provided covers data and outcomes by 10 models belonging to CMIP6. Each model has a historical archive, from 01/01/1950 to 31/12/2014 and 4 future scenarios (ssp126, ssp245, ssp370 and ssp585) ranging from 01/01/2015 to 31/12/2100. The relation of the selected models is detailed in the next Table:

Table 1. Information about the 10 climate models belonging to the 6 Coupled Model Intercomparison Project (CMIP6) corresponding to the IPCC AR6. Models were retrieved from the Earth System Grid Federation (ESGF) portal in support of the Program for Climate Model Diagnosis and Intercomparison (PCMDI).

[216+ Pages Report] The global climate tech market size is expected to grow from USD 20.06 billion in 2023 to USD 47.29 billion by 2032, at a CAGR of 10.00% from 2024-2032

1. Esri shape-file of the study area
2. shape-file of the sampled forest plots
3. Variables used for C stock modelling
4. Grid data used for C stock interpolation
5. R statistical code used for modelling

Fourth Assessment Report (AR4) Observed Climate Change Impacts, v1 (1970-2004) displays statistical significance of observed responses to climate change in the physical, terrestrial, biological systems, and marine-ecosystems. See more information at http://dx.doi.org/10.7927/H4542KJV.

BackgroundCoastal areas in Guinea-Bissau and elsewhere in West Africa are bordered by mangrove forests. In several of these places, swaths of mangrove forest have been removed and the landscape has been technologically adapted for the production of mangrove rice–a regionally important staple. However, the effects of global warming, in particular sea-level rise, pose challenges to these socioecological environments. In this context, knowledge appears as an important resource and knowing what knowledge has been produced and which perspectives have guided that production may inform future responses to climate change. We have developed a systematic literature review protocol focusing on the main question: “How have mangrove forest and mangrove rice spaces been represented in the literature on Guinea-Bissau?” The main hypothesis is that although they occupy contiguous, interrelated and interactant spaces in coastal environments, mangrove forests and mangrove rice have been studied and analyzed independently in the literature.MethodsThis is a protocol for conducting a systematic review that will include academic and non-academic literature in Portuguese, English and French. The academic literature will be retrieved from both Web of Science and Scopus using Boolean expressions. The non-academic literature will be accessed from relevant institutions, specialized libraries, and reference lists of previously selected items. Data extraction will follow a standard procedure based on an information sheet. Our analysis will be both qualitative (inductive and deductive coding, content analysis) and quantitative (word clouds, descriptive statistics and statistical testing).DiscussionThis systematic review will provide information about the conceptual framework that has been produced through research, policymaking, and conservation and development programs in the management of coastal areas. This study will identify the limitations of previous approaches and contribute to both future research and strategies for planning adaptation to climate change. Finally, the outputs will add to broader debates about people-nature coexistence and climate change adaptation and mitigation.

The property level flood risk statistics generated by the First Street Foundation Flood Model Version 2.0 come in CSV format.

The data that is included in the CSV includes:

• An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.

• The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.

• The property’s Flood Factor as well as data on economic loss.

• The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms this year and in 30 years.

• Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for this year and in 30 years.

• Information on historical events and flood adaptation, such as ID and name.

This dataset includes First Street's aggregated flood risk summary statistics. The data is available in CSV format and is aggregated at the congressional district, county, and zip code level. The data allows you to compare FSF data with FEMA data. You can also view aggregated flood risk statistics for various modeled return periods (5-, 100-, and 500-year) and see how risk changes due to climate change (compare FSF 2020 and 2050 data). There are various Flood Factor risk score aggregations available including the average risk score for all properties (flood factor risk scores 1-10) and the average risk score for properties with risk (i.e. flood factor risk scores of 2 or greater). This is version 2.0 of the data and it covers the 50 United States and Puerto Rico. There will be updated versions to follow.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

The data dictionary for the parcel-level data is below.

Field Name	Type	Description
fsid	int	First Street ID (FSID) is a unique identifier assigned to each location
long	float	Longitude
lat	float	Latitude
zcta	int	ZIP code tabulation area as provided by the US Census Bureau
blkgrp_fips	int	US Census Block Group FIPS Code
tract_fips	int	US Census Tract FIPS Code
county_fips	int	County FIPS Code
cd_fips	int	Congressional District FIPS Code for the 116th Congress
state_fips	int	State FIPS Code
floodfactor	int	The property's Flood Factor, a numeric integer from 1-10 (where 1 = minimal and 10 = extreme) based on flooding risk to the building footprint. Flood risk is defined as a combination of cumulative risk over 30 years and flood depth. Flood depth is calculated at the lowest elevation of the building footprint (largest if more than 1 exists, or property centroid where footprint does not exist)
CS_depth_RP_YY	int	Climate Scenario (low, medium or high) by Flood depth (in cm) for the Return Period (2, 5, 20, 100 or 500) and Year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_depth_002_year00
CS_chance_flood_YY	float	Climate Scenario (low, medium or high) by Cumulative probability (percent) of at least one flooding event that exceeds the threshold at a threshold flooding depth in cm (0, 15, 30) for the year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_chance_00_year00
aal_YY_CS	int	The annualized economic damage estimate to the building structure from flooding by Year (today or 30 years in the future) by Climate Scenario (low, medium, high). Today as year00 and 30 years as year30. ex: aal_year00_low
hist1_id	int	A unique First Street identifier assigned to a historic storm event modeled by First Street
hist1_event	string	Short name of the modeled historic event
hist1_year	int	Year the modeled historic event occurred
hist1_depth	int	Depth (in cm) of flooding to the building from this historic event
hist2_id	int	A unique First Street identifier assigned to a historic storm event modeled by First Street
hist2_event	string	Short name of the modeled historic event
hist2_year	int	Year the modeled historic event occurred
hist2_depth	int	Depth (in cm) of flooding to the building from this historic event
adapt_id	int	A unique First Street identifier assigned to each adaptation project
adapt_name	string	Name of adaptation project
adapt_rp	int	Return period of flood event structure provides protection for when applicable
adapt_type	string	Specific flood adaptation structure type (can be one of many structures associated with a project)
fema_zone	string	Specific FEMA zone categorization of the property ex: A, AE, V. Zones beginning with "A" or "V" are inside the Special Flood Hazard Area which indicates high risk and flood insurance is required for structures with mortgages from federally regulated or insured lenders
footprint_flag	int	Statistics for the property are calculated at the centroid of the building footprint (1) or at the centroid of the parcel (0)

R-markdown with recipe for downscaling 24-hr precipitation statistics, focusing on the two key parameters wet-day frequency and wet-day mean precipitation. The tar-archive also includes a PDF-document with results .

In recent years, the world has been facing severe challenges from climate change and environmental issues, with carbon dioxide emissions being considered one of the main driving factors. Many studies have proven that activities in various industries and fields have a significant impact on carbon dioxide emissions. However, few studies have explored the impact of gender on carbon dioxide emissions. This study aims to explore the potential impact of gender diversity on carbon dioxide emissions in the boards of directors of developed and emerging market enterprises. In addition, we also analyzed how board cultural diversity affects carbon dioxide emissions. We searched two European indices provided by Morgan Stanley Capital International (MSCI) from the Bloomberg database and conducted empirical analysis. We selected the MSCI index and MSCI emerging market index from 2010 to 2019 as samples and thoroughly cleaned up the data by removing any observations containing missing information on any variables. Statistical methods such as t-test, ordinary least squares, panel data analysis, regression analysis, and robustness testing were used for statistical analysis. At the same time, differential testing was conducted on sensitive and non-sensitive sectors, and the average representation of female boards in sensitive industries was low. The research results show that the proportion of female members on a company’s board of directors is negatively correlated with carbon dioxide emissions. This discovery is consistent with the legitimacy theory advocating for gender equality and environmental sustainability, emphasizing the importance of gender diversity in reducing greenhouse gas emissions. However, agency theory suggests that diversity may lead to internal conflicts within a company, leading to agency costs and information asymmetry. The research results show a negative correlation between board cultural diversity and carbon dioxide emissions, indicating the potential challenge of board cultural diversity. This study provides important insights for decision-makers and managers, not only inspiring corporate social responsibility and environmental policy formulation, but also of great significance for academic research in the field of climate change. Our research findings help deepen our understanding of the factors that affect carbon dioxide emissions in different sectors and countries, while also expanding the research field between gender diversity, cultural diversity, and environmental sustainability. Although this study still needs to be further expanded and deepened, it provides useful insights into the relationship between board gender and cultural diversity and carbon dioxide emissions.