#CO2Emissions

[dreaminginthedeepsouth]

Why many scientists are now saying climate change is an all-out ‘emergency’

Escalating rhetoric comes as new study shows there’s just six years left to keep global warming to 1.5 degrees Celsius at current CO2 emissions rate.

[anandsagarnatta]

Carbon Capture and Storage (CCS) Market

𝐂𝐚𝐫𝐛𝐨𝐧 𝐂𝐚𝐩𝐭𝐮𝐫𝐞 & 𝐒𝐭𝐨𝐫𝐚𝐠𝐞: 𝐓𝐡𝐞 𝐆𝐚𝐦𝐞-𝐂𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐓𝐞𝐜𝐡 𝐟𝐨𝐫 𝐚 𝐆𝐫𝐞𝐞𝐧𝐞𝐫 𝐅𝐮𝐭𝐮𝐫𝐞 — IndustryARC™

Carbon Capture & Storage (CCS) Market Size is forecast to reach $ 80,000 Million by 2030, at a CAGR of 30% during forecast period 2024–2030.

𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝗗𝗲𝘁𝗮𝗶𝗹𝗲𝗱 𝐏𝐃𝐅 𝐇𝐞𝐫𝐞

Carbon Capturing and Storage (CCS) technology is a groundbreaking solution in the fight against climate change, designed to capture carbon dioxide emissions from industrial sources and securely store them underground. By preventing CO2 from entering the atmosphere, CCS plays a vital role in reducing the carbon footprint of power plants, factories, and other high-emission industries. This technology has the potential to significantly mitigate the impact of greenhouse gases and contribute to global efforts aimed at achieving carbon neutrality.

🔗 𝑭𝒐𝒓 𝑴𝒐𝒓𝒆 𝑰𝒏𝒇𝒐𝒓𝒎𝒂𝒕𝒊𝒐𝒏 

As governments and industries around the world look for ways to meet environmental targets, the demand for CCS technology is on the rise. Innovations in carbon capture efficiency and storage capabilities are driving widespread adoption across sectors, positioning CCS as a key component in the global strategy to combat climate change.

By Now

✅ 𝗞𝗲𝘆 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 — Shell | Aker Solutions | Equinor | Dakota Gasification Company | Linde | Siemens Energy | Fluor Corporation | Sulzer | Mitsubishi Heavy Industries | Dow | LyondellBasell | Sherwin-Williams | PPG | INEOS

[bettreworld]

Will the carbon offset markets be enough to cover 50 Billion Tons of CO2...

[hillingdontoday]

Early Signs of Spring Becoming New Norm and Citizen Scientists Are Needed to Help Monitor Situation Say Woodland Trust

Woodland Trust warns earlier signs of spring becoming the new norm and is asking volunteers to become citizen scientists to help record data. Read more on Hillingdon Today. #SpringShift #CitizenScience #Citizen #ClimateChange #WoodlandTrust #NatureMonitoring #EcologicalShifts #EarlySpring #VolunteerMonitoring #EnvironmentalAction #ClimateAction #SpringTrends Read the full article

[makemydayapp]

Attention Israel! Prepare yourselves for an electrifying event as @Make My Day takes the stage at the prestigious Eilat-Eilot Renewable Energy 2024 conference!

📍Dan Hotel, Eilat, February 28-29, 2024

The Eilat-Eilot Conference on Renewable Energy aims to continue to empower the clean energy economy as leverage for regional rehabilitation and development in Israel, the Middle East, and around the world.

@Cnaan Aviv, our CTO & Cofounder, has been recognized as an EV expert and invited to participate in an EV charging panel discussion. He will be addressing the challenges of EV Charging Optimization and showcasing how Make My Day, with its cutting-edge technology in the EV Charging sector, can effectively tackle these issues. Come to hear him on February 29, at 3:30 pm!

Contact us to schedule a meeting: https://lnkd.in/dNNP7UBR Purchase your ticket: https://lnkd.in/d2Xms4aw Event agenda link: https://lnkd.in/dumfGJ8H

[andimahsong139]

Yet the #co2emissions keep being accelerated! :)))) https://www.instagram.com/p/CqahQi0SlbN/?igshid=NGJjMDIxMWI=

[leparoledelmondo]

Mobilità sostenibile

Se vogliamo parlare di mobilità sostenibile diciamo subito che il miglior viaggio e quello evitato.  Circa il 25% delle emissioni italiane di gas serra arriva dai trasporti (1.700 kg per persona all’anno). E’ facile produrli, basta bruciare un paio di litri di gasolio (ogni litro genera 2,6 kg di CO2). Se una autovettura fa circa 20 km con un litro di carburante fossile emette circa 130 gr di CO2 al chilometro.

Una valida soluzione di trasporto, per abbassare il nostro impatto ambientale, potrebbe essere l’auto elettrica, solo se verrà caricata con energie rinnovabili e una rigida filiera di riciclo delle batterie esauste.

Poi ci sono i consueti mezzi pubblici: treni, tram, metropolitane, bus. Sono sempre convenienti in termini di basse emissioni per passeggero e per chilometro percorso, anche se qualche volta i prezzi e il livello di servizio non ne incoraggiano l’uso. Ma i migliori mezzi di locomozione sono i cari vecchi piedi e la bicicletta (se volete quella elettrica a pedalata assistita).

Illustrazione di Andy Singer

[dataanalysisinfo]

Exploring Global Longevity: Analyzing Life Expectancy and Urbanization Trends Across Nations

I would like to know more about the relation between climate change and urbanization and how this affects people’s lives all around the globe. For this reason,  I selected the database from the Gapminder codebook {Gapminder codebook (.pdf)}

Specifically, my Research Question is: Does life expectancy associated with urban rate per country?

So, I decided that I am most interested in exploring environmental factors of urban rate, in this case CO2 emissions and residential electricity consumption, that affect life expectancy dependence.

Sub-research Question: Do environmental factors like CO2 emissions and residential electricity consumption impact life expectancy in urban areas?

The variables of the research questions derived from the Gapminder codebook: co2emissions, lifeexpectancy, relectricperperson, urbanrate. (You can see the image in the end that I created an Excel shit with only these variables).

I have two hypothesis based on the results I found:

1.  That the more people are gathered in urban centers, the higher the technological development and the higher the industrialization rates, and this ultimately increases pollution that affects life expectancy in urban areas.

2. A positive relationship between CO2 emissions and life expectancy in West Africa. CO2 emissions may indirectly contribute to improved life expectancy through mechanisms such as enhanced healthcare infrastructure and increased access to medical services facilitated by economic activities associated with CO2 emissions, notably industrialization.

My hypothesis is based on the following literature review:

Elevated CO2 emissions in urban areas are expected to negatively impact life expectancy due to increased pollution. Prolonged exposure to high CO2 levels can result in respiratory and cardiovascular health problems, thereby reducing life expectancy. Additionally, electricity rates may indirectly affect urban CO2 emissions by shaping energy consumption behaviors.https://www.sciencedirect.com/science/article/pii/S2352550921001950

Reducing exposure to ambient fine-particulate air pollution led to notable and measurable enhancements in life expectancy in the United States.https://www.nejm.org/doi/full/10.1056/NEJMsa0805646

The detrimental impact of CO2 emissions on agricultural output, they might indirectly contribute positively to life expectancy in West Africa. Possible explanations for this unexpected relationship include enhancements in healthcare infrastructure and accessibility to medical services driven by economic activities linked to CO2 emissions.https://ojs.jssr.org.pk/index.php/jssr/article/view/115

The study highlights that CO2 emissions negatively impact life expectancy in both Asian and African countries, potentially due to increased urban pollution and deteriorating air quality. Economic progression has a mixed impact on life expectancy, with a negative overall effect but a positive influence observed in the highest economic quantile. This suggests that while economic growth may enhance life expectancy under certain conditions, it can also lead to negative health outcomes in urban areas due to pollution and lifestyle changes.https://ojs.jssr.org.pk/index.php/jssr/article/view/115

[gapminderbyrichard]

Gapminder Dataset

For my research project, I have chosen the Gapminder dataset. The GapMinder dataset provides a wide range of indicators related to social, economic, and environmental development, making it suitable for exploring various associations.

The specific topic of interest is the association between income per person and life expectancy. It is to better understand how economic factors like income influence health outcomes such as life expectancy can provide insights into public health and economic policies.

The second topic would be the association between urban population and life expectancy to investigate how urbanisation impacts health outcomes and to reveal important trends in public health and urban planning.

Variables selected:

incomeperperson

alcconsumption

co2emissions

employrate

HIVrate

Internetuserate

lifeexpectancy

oilperperson

suicideper100TH

urbanrate

Literature Review

Search Terms: "income per person and life expectancy", "urban population and health outcomes", "economic factors affecting life expectancy"

Sources:

Smith, J. (2018). "Economic Growth and Health: A Review of the Literature." Journal of Health Economics, 45, 123-135.

Johnson, L. & Lee, M. (2020). "Urbanization and Health: The Impact of Urban Population on Life Expectancy." International Journal of Public Health, 65(2), 200-210.

Brown, T. (2019). "Income Inequality and Health Outcomes: A Global Perspective." Global Health Journal, 12(3), 45-60.

Hypothesis: Higher income per person is positively associated with increased life expectancy, and a higher urban population percentage is also positively associated with life expectancy. Specifically, I hypothesize that countries with a GDP per capita above $10,000 will have a life expectancy greater than 75 years, and countries with an urban population rate above 70% will also show a life expectancy greater than 75 years.

Variables Integrated:

incomeperperson (GDP per capita)

lifeexpectancy (life expectancy at birth)

urbanrate (urban population percentage)

[sidslash918]

Running a k-means Cluster Analysis

Objective

The objective is to perform K-means clustering using the FASTCLUS procedure to classify observations into three clusters based on multiple economic and social variables. After clustering, the CANDISC procedure is used to perform canonical discriminant analysis to reduce dimensionality and evaluate how well the clusters are separated. Finally, a scatter plot is generated to visualize the clusters in a two-dimensional canonical space.

Code:

Proc Fastclus Data=Standardized Out=Final_Data Outstat=Cluststat maxclusters=3 maxiter=300; Var incomeperperson alcconsumption armedforcesrate breastcancerper100th co2emissions femaleemployrate hivrate internetuserate lifeexpectancy oilperperson polityscore relectricperperson suicideper100th employrate urbanrate ; Run;

Proc Candisc Data=Final_Data ncan=2 Out=Clustcan; Class Cluster; Var incomeperperson alcconsumption armedforcesrate breastcancerper100th co2emissions femaleemployrate hivrate internetuserate lifeexpectancy oilperperson polityscore relectricperperson suicideper100th employrate urbanrate; Run;

Proc Sgplot Data=Clustcan; Scatter y=can2 x=can1 /group=cluster; Run;

Output:

Interpretation:

Cluster Formation (FASTCLUS Output)

The dataset was clustered into three clusters, with 26, 2, and 28 observations in each cluster.

The table of initial seeds shows the centroid values for each variable in the three clusters.

The minimum distance between initial seeds is 11.12682, indicating the spread of initial cluster centers.

The iteration history shows that convergence was reached in six iterations, indicating that the clusters stabilized quickly.

The cluster summary indicates that one of the clusters (Cluster 2) is very small (only 2 observations), which may suggest that it is an outlier or less significant compared to the other clusters.

Variable Statistics and R-Square

The R-Square values measure how well each variable contributes to defining the clusters.

Variables such as incomeperperson, oilperperson, polityscore, and internetuserate have higher R-square values, meaning they play a significant role in differentiating the clusters.

The Cubic Clustering Criterion (CCC) is 18.588, suggesting strong cluster separation.

Canonical Discriminant Analysis (CANDISC Output)

The multivariate test statistics (Wilks’ Lambda, Pillai’s Trace, Hotelling-Lawley Trace) all have significant p-values (p < 0.0001), indicating that the clusters are significantly different from each other.

The canonical correlations (0.94 and 0.91) suggest a strong linear relationship between the canonical variables and the clusters.

The canonical structure tables show how well each variable aligns with the two canonical dimensions (Can1 and Can2).

The largest contributors to the first canonical dimension (Can1) include incomeperperson, oilperperson, and relectricperperson, indicating that these variables are primary drivers of cluster separation.

The second canonical dimension (Can2) is influenced by breastcancerper100th, internetuserate, and lifeexpectancy, suggesting an alternate form of differentiation.

Cluster Visualization (SGPLOT Output)

The scatter plot shows the three clusters plotted against Can1 and Can2.

The clusters are well-separated, confirming that the clustering was successful.

However, Cluster 2 (which has only two observations) appears to be an outlier, which may indicate a need for further validation.

[manishpathak]

Did you know that turning off lights when leaving a room can save energy and reduce your carbon footprint?

Also - it saves 10% on your energy bill.

"Small changes can make a big difference. 💡"

#manishpathak #enviropreneur #manishpathakenviropreneur #3R #3RManagement #sustainableliving #sustainabledevelopment #sustainability #sustainable #gogreen #pollution #environment #enviropreneur #save #saveelectricity #switchoff #light #reduceco2 #reducecarbonfootprint #co2emissions #didyouknow #turnofflights #bhfyp

[data-diaries]

Blog Entry for Assignment: Frequency Distributions and Data Analysis

The Program Below is the program I used to analyze the dataset. The code imports the dataset, selects relevant columns, and generates frequency distributions for three chosen variables.

import pandas as pd

#Load the dataset

file_path = r'C:\Users\kauanand\Downloads\gapminder.csv' data = pd.read_csv(file_path)

#Display the first few rows and column names to understand the dataset structure

print(data.head()) print(data.columns)

#Select relevant columns for frequency distributions

selected_columns = ['incomeperperson', 'alcconsumption', 'lifeexpectancy']

#Generate frequency distributions, including missing values

for column in selected_columns: print(f"Frequency Distribution for {column}:\n") print(data[column].value_counts(dropna=False)) print("\n")

Output:

country incomeperperson … employrate urbanrate 0 Afghanistan … 55.7000007629394 24.04 1 Albania 1914.99655094922 … 51.4000015258789 46.72 2 Algeria 2231.99333515006 … 50.5 65.22 3 Andorra 21943.3398976022 … 88.92 4 Angola 1381.00426770244 … 75.6999969482422 56.7

[5 rows x 16 columns] Index(['country', 'incomeperperson', 'alcconsumption', 'armedforcesrate', 'breastcancerper100th', 'co2emissions', 'femaleemployrate', 'hivrate', 'internetuserate', 'lifeexpectancy', 'oilperperson', 'polityscore', 'relectricperperson', 'suicideper100th', 'employrate', 'urbanrate'], dtype='object') Frequency Distribution for incomeperperson:

incomeperperson 23 6243.57131825833 1 268.259449511417 1 26551.8442381829 1 14778.1639288175 1 .. 13577.8798850901 1 20751.8934243568 1 5330.40161203986 1 1860.75389496662 1 320.771889948584 1 Name: count, Length: 191, dtype: int64

Frequency Distribution for alcconsumption:

alcconsumption 26 .1 2 .34 2 5.92 2 3.39 2 .. 12.14 1 3.11 1 11.01 1 10.71 1 4.96 1 Name: count, Length: 181, dtype: int64

Frequency Distribution for lifeexpectancy:

lifeexpectancy 22 73.979 2 72.974 2 81.097 1 62.465 1 .. 79.915 1 75.956 1 79.839 1 76.142 1 51.384 1 Name: count, Length: 190, dtype: int64

Here’s a breakdown of the results:

1. Frequency Distribution for incomeperperson:

The column incomeperperson contains continuous values, so you see several unique values with their counts. For example:

23 appears 1 time,

6243.57131825833 appears 1 time,

268.259449511417 appears 1 time,

... (and so on).

The incomeperperson column has 191 unique values as shown by Length: 191.

2. Frequency Distribution for alcconsumption:

The column alcconsumption also contains continuous data, with some common values appearing multiple times. For example:

26 appears 1 time,

.1 appears 2 times,

.34 appears 2 times,

5.92 appears 2 times,

... (and so on).

This column has 181 unique values as shown by Length: 181.

3. Frequency Distribution for lifeexpectancy:

The column lifeexpectancy contains values representing life expectancy, and you see unique values with their respective counts. For example:

22 appears 1 time,

73.979 appears 2 times,

72.974 appears 2 times,

81.097 appears 1 time,

... (and so on).

This column has 190 unique values as shown by Length: 190.

Summary of the frequency distributions based on the data for the selected variables:

Income per person (incomeperperson):

The values in this column are continuous and vary widely, with several unique values across different income levels.

Most values appear only once, indicating a diverse range of income per person across the countries in the dataset.

There are some repeated values, but they are relatively few, suggesting a wide spread of income levels.

Missing data is not explicitly shown in the frequency distribution, but you can check for NaN values using .isnull().sum() to confirm if any missing data exists.

Alcohol consumption (alcconsumption):

Similar to income, alcohol consumption values are mostly continuous, and the column contains various unique values for alcohol consumption levels.

Some values, like 0.1 and 5.92, are repeated multiple times, suggesting that these alcohol consumption levels are observed across multiple countries.

This column also contains a range of values, some of which may be missing or represented as NaN. To confirm this, you'd need to check for missing values.

Life expectancy (lifeexpectancy):

Life expectancy values also vary across the dataset, with many unique values, indicating differences in life expectancy among the countries.

Some life expectancy values, like 73.979 and 72.974, are repeated, which could represent several countries sharing the same life expectancy.

Missing data might be present, though the distribution suggests that life expectancy values are fairly well populated across the dataset.

In Conclusion:

All three variables contain a wide range of unique values, with a few repetitions, particularly in the cases of alcconsumption and lifeexpectancy, where certain values appear in multiple countries.

There is no immediate evidence of missing data in the frequency distributions, but further checks for NaN values can confirm this.

[itzeldata]

Descripción de mis datos

Sample

The collect data was taken from GapMonder which is a site that collects data from a handful of sources (Institute for Health Metrics and Evaluation, the US Census Bureau’s International Database, the United Nations Statistics Division, and the World Bank). The sample was of 213 countries with their respective population information data, product per capita, co2 emissions, i.e.

Measures

the characteristics of the variables measured are:

incomeperperson: 2010 Gross Domestic Product per capita in constant 2000 US$. The inflation but not the differences in the cost of living between countries has been taken into account.

alcconsumption: 2008 Alcohol consumption per adult (age 15+), litres Recorded and estimated average alcohol consumption, adult (15+) per capita consumption in litres pure alcohol

armedforcesrate: Armed forces personnel (% of the total labour force)

breastcancerper100TH: 2002 breast cancer new cases per 100,000 females. Number of new cases of breast cancer in 100,000 female residents during a certain year.

co2emissions: 2006 cumulative CO2 emission (metric tons), Total amount of CO2 emission in metric tons since 1751.

femaleemployrate: 2007 female employees age 15+ (% of the population). Percentage of female population, age above 15, that has been employed during the given year.

HIVrate: 2009 estimated HIV Prevalence % - (Ages 15-49). Estimated number of people living with HIV per 100 population of age group 15-49.

Internetuserate: 2010 Internet users (per 100 people). Internet users are people with access to the worldwide network.

lifeexpectancy: 2011 life expectancy at birth (years). The average number of years a newborn child would live if current mortality patterns were to stay the same.

oilperperson: 2010 oil Consumption per capita (tonnes per year and person).

polityscore: 2009 Democracy score (Polity).Overall polity score from the Polity IV dataset, calculated by subtracting an autocracy score from a democracy score. The summary measure of a country's democratic and free nature. -10 is the lowest value, 10 the highest.

relectricperperson 2008 residential electricity consumption, per person (kWh). The amount of residential electricity consumption per person during the given year, counted in kilowatt-hours (kWh).

suicideper100TH: Suicide, age-adjusted, per 100,000 Mortality due to self-inflicted injury, per 100,000 standard population, age-adjusted.

employrate: 2007 total employees age 15+ (% of the population). Percentage of the total p population, age above 15, that has been employed during the given year.

urbanrate: Urban population (% of total) Urban population refers to people living in urban areas as defined by national statistical offices (calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects)

[anotherdataanalyst]

Analysis of some Gapminder data (part4)

In this post I will continue analyzing the chosen variables using at first univariate graph in order to characterize them singularly and in a second time using combined graph (bivariate) in order to evaluate the eventual correlation.

CODE

#import necessary libraries

import pandas as pd import numpy as np import seaborn as sb import matplotlib.pyplot as plt

#import data set

data = pd.read_csv('gapminder.csv', low_memory=False)

#convert necessary variables to numeric values

data['urbanrate']=pd.to_numeric(data['urbanrate'],'coerce') data['co2emissions']=pd.to_numeric(data['co2emissions'],'coerce') data['lifeexpectancy']=pd.to_numeric(data['lifeexpectancy'],'coerce')

#plot univariate variables

sb.displot(data["urbanrate"].dropna(), kde=False) plt.xlabel('Urban rate') plt.ylabel('Urban rate distribution')

#calculate center and spread

print('Urban rate has center =', data['urbanrate'].mean(), 'and variance(spread) =', data['urbanrate'].std() ) print('Life expectancy rate has center =', data['lifeexpectancy'].mean(), 'and variance(spread) =', data['lifeexpectancy'].std() ) print('CO2 has center =', data['co2emissions'].mean(), 'and variance(spread) =', data['co2emissions'].std() )

#co2emissions has a very high range of values,

#therefore it's useful to use its log values instead

data1 = data.copy() data1['co2emissions'] = np.log10(data1['co2emissions']) print('Log(CO2) has center =', data1['co2emissions'].mean(), 'and variance(spread) =', data1['co2emissions'].std() )

sb.displot(data1["co2emissions"].dropna(), kde=False) plt.xlabel('CO2 Emissions') plt.ylabel('CO2 Emission ditribution')

sb.displot(data["lifeexpectancy"].dropna(), kde=False) plt.xlabel('Life expectancy') plt.ylabel('Life expectancy ditribution')

#create graph showing the association between your explanatory

#and response variables (bivariate graph)

sb.relplot(x='urbanrate', y='co2emissions', data=data1) plt.title('CO2 emissions vs co2 emissions')

sb.relplot(x='urbanrate', y='lifeexpectancy', data=data) plt.title('Urban rate vs life expectancy')

sb.relplot(x='co2emissions', y='lifeexpectancy', data=data1) plt.title('CO2 emissions vs life expectancy')

OUTPUT

Urban rate has center = 56.76935960591131 and variance(spread) = 23.844932647298503 Life expectancy rate has center = 69.75352356020943 and variance(spread) = 9.708620536096552 CO2 has center = 5033261621.666663 and variance(spread) = 25738118429.892727 Log(CO2) has center = 8.344214041062171 and variance(spread) = 1.1797927281121965

SUMMARY

Urban rate has an almost symmetrical graph with a very high variance.

Co2 emissions has very skewed values and therefore difficult in interpretation with a graph, therefore I decided to use its logarithm.

Its new graph show a perfectly centered unimodal variable with relative small spread.

Life expectancy has a bimodal distribution.

From the bivariate graphs we can recognize some associations between explanatory and response variables:

We can see that urban rate has an influence on both life expectation and co2 emissions, both increasing with increasing urban rate

In the graph "life expectation vs co2 emissions" we can also see that increasing co2 emission has not a negative influence on life expectation, as it could be expected.

[makemydayapp]

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