#Tree Equity Score

[detroitography]

Detroit By The Numbers: April 2025 Data Roundup

25,000 trees planted as part of the Tree Equity Partnership’s goal of 75,000 by 2027 (WDET) 56 mentions of “neighborhood” by Mayor Duggan in his State of the City speech in 2024 after a previous high of 43 in 2015 (DETROITography/Ted Tansley) Check out the data on DetroitData.org 28th round of Motor City Match awards to 13 new brick-and-motor businesses (Detroit Chamber) 6,164 visitors on…

[atompowers]

🌞 3 Treemendously Simple Sustainable City Living Scoring Tools

[elsa16744]

What Are ESG Funds? 

Investors screen companies based on their sustainability compliance scores, and ESG metrics have enabled investment firms to satisfy investor requirements. One result of the increased focus on corporate sustainability is ESG funds. This post will describe different components of ESG funds with examples. 

What is an ESG Fund? 

ESG funds are financial investment vehicles offered by private equity firms, mutual fund managers, and portfolio management solutions. These funds utilize environmental, social, and governance indicators to prioritize sustainable companies in their stock selection. 

However, ESG metrics and performance calculation methods vary across regional sustainability accounting frameworks. So, investors and business owners depend on ESG consulting to evaluate their compliance ratings. 

Consider that an investment fund, company stock, bond, or real estate project claims to comply with ESG criteria. Investors will require objective data analytics to cross-examine the validity of such claims. Besides, sustainability benchmarking can reveal other investment opportunities with a better balance between ESG compliance and business growth potential. 

Nevertheless, many ESG funds utilize strategies like excluding corporations known for ethically ambiguous practices and offerings. For example, an ESG fund can avoid including an alcohol business in its portfolio due to the social impact concerns. 

Types of ESG Funds 

1| Ethical Funds 

Consulting firms can help you shortlist the funds that use morality, social ethics, faith, and a broader concept of “doing good.” Such mutual funds are ethical funds, and ESG solutions can help investors study more holistic data and their performance. 

Each society has unwritten rules, such as keeping children safe or respecting elderly citizens. These values drive investor behavior, resulting in the rise of ethical investing. Imagine high net-worth individuals (HNWI) investing in an ethical fund after a social impact analysis. The “benefit” emphasizes the religious, moral, and political gains rather than returns. 

Consider an ethical fund that utilizes the raised funds to eradicate the malnutrition crisis in the world’s underdeveloped areas. Some investors will use their political views to determine companies that deserve financial assistance. 

These concepts often correlate with intangible gains like the religious concepts of virtues and vices. Therefore, some investors request ESG consulting firms to screen ethical funds irrespective of a lower return on investment (ROI). 

2| Social Impact Funds 

Social impact investing involves corporate stocks related to renewable energy companies or forest and biodiversity conservation. ESG solutions can research the socially positive impact of an enterprise to evaluate whether it qualifies to be in the investment portfolio of social impact funds. 

While ethical ESG funds investors leverage religious, moral, or political investor philosophies, social impact funds exclusively emphasize how an investment benefits society. For example, supporting vocational e-learning platforms increases the economic competitiveness of a demographic. 

Likewise, some social impact funds garner capital support from non-governmental organizations (NGOs), insurance companies, banks, cooperative societies, and HNWIs. ESG consulting firms consider the social impact funds advantageous due to a more objective outlook driven by tangible gains. 

After all, quantifying and modeling statistical data on literacy rates, rehabilitated substance abusers, or renewable energy research outcomes is possible via appropriate ESG solutions. 

3| Green Funds 

Green funds select portfolio companies by studying the environmentally harmful or beneficial effects of corporate activities. For example, ineffective waste management causes pollution of water bodies. If an animal or human consumes water from these resources, they become ill. Polluted water can also damage trees through soil seepage near the roots. 

Investors want to support organizations that realize the ecological cost of industrial development. Such companies always discover recycling and waste reduction technologies. Therefore, ESG consulting firms list green funds as the ones that include only environmentally responsible brands in the portfolio. 

Nevertheless, the performance of a green fund will fluctuate due to market trends. You want to balance environmentalist investor activism with holistic risk management. Otherwise, your capital resources will become available to a less reliable enterprise. If an investor experiences a significant loss due to green fund investments, their ability to support other eco-friendly brands diminishes. 

Green funds still witness a rise in demand because more investors are utilizing ESG solutions to screen the companies working on renewable energy, forest preservation, pollution analytics, and animal protection projects. 

Screening Strategies Employed by the Best ESG Funds 

1| Compliance Benchmarking 

An ESG score relies on the company’s performance across sustainability accounting metrics. You can estimate it using statistical models. Still, different ESG solutions will develop proprietary performance assessment methods. Therefore, investors must monitor multiple online databases to determine whether a company is committed to sustainable development goals. 

Compliance benchmarking uses a single performance management system to determine ESG scores. It reveals the business risks associated with unsustainable operations. So, the manager can selectively address these issues that reduce their ESG score. 

A benchmark involves reference values to help with progress monitoring over time. Managers and investors require compliance benchmarking to check how a company has improved its ESG performance. The ESG Funds leverage benchmarking when selecting stocks for their portfolio. 

2| Peer Analytics 

Two eco-friendly companies can have significant differences across ESG performance metrics. Likewise, businesses working in different industries might exhibit identical ESG compliance ratings. However, comparing them with their business rivals in the same industry gives you a clearer estimate of their sustainability. 

Peer analytics investigates multiple organizations to identify the best fit for investors’ preferences and risk profiles. You can quickly learn about which company tops the environmental compliance charts. Later, ESG funds use these insights to distribute their financial resources across the most sustainable companies. 

3| Greenwashing Inspections 

A brand’s reputation as an ESG-first enterprise must be authentic. Verifying the validity of what a company claims as its sustainability performance can assist the investors in separating the gene the genuinely eco-friendly organizations from the companies that apply greenwashing tactics. 

Greenwashing is a result of unethical marketing and ESG report manipulation. It includes creating and falsifying sustainability compliance datasets. So, the company’s compliance ratings seem better than the accurate scores. Professional ESG consulting firms always inspect sustainability disclosure documents to identify greenwashing attempts. 

4| Controversy Intelligence 

Historical performance records associated with an organization can be instrumental in verifying the legitimacy of its ESG compliance claims. Controversy research and intelligence gathering will allow the fund managers to audit a company’s brand presence across multiple media outlets. 

Innovative ESG solutions exist today, featuring scalable social listening capabilities and press coverage analytics. Their essential services include tracking how often publications and social media mention a corporate brand. 

Investment strategists can also benefit from more advanced social media listening tools like sentiment analytics and materiality assessment. For example, an organization might have an attractive ESG score greater than 90. Simultaneously, some controversial events could have a particular connection with this organization, and ESG funds will consider it in screening. 

Examples of ESG Funds 

1| Joint Sustainable Development Goals (SDGs) Fund 

The United Nations (UN) created a financial vehicle known as SDG Fund in 2014. This financial mechanism used to have many backers among the UN’s member countries and philanthropists when it was operational. However, the Joint SDG Fund is its latest spiritual successor. It also champions a multi-dimensional cooperative approach to address sustainability integration challenges. 

Several agencies help United Nations deliver on-ground support to the marginalized, financially weak, and old individuals in over 23 geopolitical territories through this fund. The Joint SDG Fund concentrates on solving the contemporary social-economic and environmental challenges by promoting the following. 

Universal access to authoritative educational resources on climate change, 

Social protection systems for the workers in informal sectors, 

Scientific breakthroughs vital for sustainable development, 

Energy-efficient technologies and research innovations, 

Disaster risk management and response strategies, 

Availability of clean drinking water. 

The characteristics of the joint sustainable development goals fund qualify it as an ESG fund. Therefore, some ESG consulting firms recommend this financial vehicle to environmentally conscious investors. 

2| Vanguard FTSE Social Index Fund (VFTAX) 

VFTAX tracks US Select Index Series termed FTSE4Good. The Financial Times Stock Exchange (FTSE) index series emphasizes environmental, social, and governance practices. So, VFTAX utilizes this resource to screen portfolio companies and corresponding stocks. 

This ESG fund excludes the enterprises creating “vice products” like gambling, adult entertainment, tobacco, and addictive beverages. Investors will observe that VFTAX also avoids corporations relying heavily on non-renewable energy resources. 

Besides, any company involved in controversies and discriminatory practices will not make it into the VFTAX portfolio. Moreover, it excludes businesses creating weapons systems for the military and civilians. 

VFTAX has a low expense ratio. The minimum investment value is 3000 USD. Institutional investors should also consider VFTNX related to this social index fund, requiring 5 million US dollars. Its portfolio comprises Amazon Inc., Alphabet Inc., Microsoft Corp, and Apple Inc. 

Conclusion 

ESG funds utilize sustainability accounting frameworks for portfolio management. Investors conscious about how companies affect the world prefer ESG-based investment strategies. Therefore, modern ESG consulting firms develop statistical models to quantify corporate compliance across sustainability metrics. 

Mitigating carbon risks, affordable Healthcare, rehabilitating substance abusers, and offering universal access to clean water are the admirable objectives of sustainable businesses. High net-worth individuals (HNWI) and institutional investors also want to make a positive impact. 

So, ESG funds allow them to cooperate for ethical, religious, political, social, environmental, and humanitarian development. Still, compliance assessment, monitoring, and reporting remind advanced technological assistance offered by talented domain experts. 

A leader in ESG solutions, SG Analytics, empowers organizations and investment managers to conduct holistic analytical operations for sustainability reporting and impact investing. Contact us today for automated multilingual analytics across 1000+ indicators to increase compliance ratings. 

[bimbosymzs]

TYSK: You´re missing out on a lot

TYSK, or Things You Should Know, is a bi-weekly post I intend to share about worldwide news. I am that random factoid friend, and I love sharing new information with people because I see information as power. This is my way of attempting to do my part in sharing and participating in the world and whatnot.

🗞🗞🗞🗞🗞🗞🗞🗞🗞🗞🗞🗞🗞🗞🗞🗞🗞🗞🗞🗞🗞🗞🗞🗞🗞

Insight

I recently watched the film American Fiction, I am a fan of everyone involved and am in love with the premise of the film. I did find a bit of disappointment with Tracee Ellis Ross's character's quick intro and exit. There was more to explore with her character and I was deeply disappointed. The film does well at highlighting the interpersonal issues of Black middle-class families. It asks us to be more nuanced about the lives of Black Americans and the different ways we suffer under capitalism/white supremacy. We need to take a moment to be more curious about who is telling our stories and how we can have more of us in the room. Equity is necessary and best when done right.

News

Yemeni and US/UK conflict is suspected to be caused by Yemen´s stance against the genocide of the Palestinian people.

2. Rihanna´s brand SavageX Fenty is reported to have an ethics rating lower than Sheins. Re/Make´s annual ethics report, the Fashion Accountability Report scored it a 4/150. While doing well for diversity, they noted that there was little information about the care for its workers and the earth. Along with her makeup line, there are reports of child labor law violations, volatile work environments, and a history of using oil-based synthetic products.

3. It was recently discovered after the bodies of 3 Black men were found in a Pauper´s grave in Hinds County, that more than 600 other bodies had been buried there since 2008. Since 2016, over 215 bodies have been placed there in unmarked graves. This information surfaced after a mother had been informed that her son had been killed in a hit-and-run by an off-duty officer.

Art

Maya Angelou´s ¨Caged Bird¨

A free bird leaps

on the back of the wind   

and floats downstream   

till the current ends

and dips his wing

in the orange sun rays

and dares to claim the sky.

But a bird that stalks

down his narrow cage

can seldom see through

his bars of rage

his wings are clipped and   

his feet are tied

so he opens his throat to sing.

The caged bird sings   

with a fearful trill   

of things unknown   

but longed for still   

and his tune is heard   

on the distant hill   

for the caged bird   

sings of freedom.

The free bird thinks of another breeze

and the trade winds soft through the sighing trees

and the fat worms waiting on a dawn bright lawn

and he names the sky his own.

But a caged bird stands on the grave of dreams   

his shadow shouts on a nightmare scream   

his wings are clipped and his feet are tied   

so he opens his throat to sing.

The caged bird sings   

with a fearful trill   

of things unknown   

but longed for still   

and his tune is heard   

on the distant hill   

for the caged bird   

sings of freedom.

[researchbuzz]

National Shade Map, USAID Funding Cuts Impact, Google AI Errors, More: Wednesday ResearchBuzz, June 4, 2025

NEW RESOURCES UCLA: National shade map from UCLA and American Forests launched to combat deadly urban heat. “The Shade Map, now available through American Forests’ Tree Equity Score platform, shows where shade exists — and where it doesn’t — in 101 of the largest urbanized areas in the country, covering more than 360 cities and towns. It visualizes how shade from trees and buildings changes…

[burdsandwurds]

i often daydream about planting trees. sometimes, i literally try to plan out buying neglected farmland and turning it into a forest or park or something. naturally, i tend to think quite a bit about green spaces in our cities and i often mess around with american forests’ tree equity score calculator ever since it came out.

recently, the city of newport kentucky got a $1 million federal grant to create more green space in neighborhoods that are sorely lacking. they’re even going as far as to tear up unnecessary concrete to create more space for trees.

every time i think about this, i smile a little. i’ll have to head down there a few times in the next few years to see how the transformation is going.

[mengchunhsieh]

Assignment 2:

Finding important features to predict the personal equity plan (PEP) in the banking system

Introduction:

A personal equity plan (PEP) was an investment plan introduced in the United Kingdom that encouraged people over the age of 18 to invest in British companies. Participants could invest in shares, authorized unit trusts, or investment trusts and receive both income and capital gains free of tax. The PEP was designed to encourage investment by individuals. Banks engage in data analysis related to Personal Equity Plans (PEPs) for various reasons. They use it to assess the risk associated with these investment plans. By examining historical performance, market trends, and individual investor behavior, banks can make informed decisions about offering PEPs to their clients.

In general, banks analyze PEP-related data to make informed investment decisions, comply with regulations, and tailor their offerings to customer needs. The goal is to provide equitable opportunities for investors while managing risks effectively. 

SAS Code

proc import out=mydata

    datafile="/home/u63879373/bank.csv"

    dbms=csv

    replace;

    getnames=YES;

PROC SORT; BY id;

ods graphics on;

PROC HPFOREST;

target PEP/level=nominal; /*target variable*/

input sex region married children car save_act current_act mortgage /level=nominal;

input age income /level=interval ;  

RUN;

Dataset

The dataset I used in this assignment contains information about customers in a bank. The Data analysis used will help the bank take know the important features that can affect the PEP of a client from the following features: age, sex, region, income, married, children, car, save_act, current_act and the mortgage.

Id: a unique identification number,

age: age of customer in years (numeric),

income: income of customer (numeric)

sex: MALE / FEMALE

married: is the customer married (YES/NO)

region: inner_city/rural/suburban/town

children: number of children (numeric)

car: does the customer own a car (YES/NO)

save_acct: does the customer have a saving account (YES/NO)

current_acct: does the customer have a current account (YES/NO)

mortgage: does the customer have a mortgage (YES/NO)

Figure1: dataset

Random Forest

Random forests are predictive models that allow for a data driven exploration of many explanatory variables in predicting a response or target variable. Random forests provide important scores for each explanatory variable and also allow you to evaluate any increases in correct classification with the growing of smaller and larger number of trees.

Random forest was used on the above dataset to evaluate the importance of a series of explanatory variables in predicting a binary, categorical response variable (PEP).

Following the import to import the dataset, I will include PROC HPFOREST. Next, I name my response, or target variable, PEP which is a nominal variable.

For the input statements I indicate the following:

Sex, region, married, children, car, save_act, current_act, and mortgage are nominal variables (categorical explanatory variables).

Age and income are interval variables (Quantitative explanatory variables).

Random Forest Analysis:

After running the code above we get the following results:

Model information

Figure2: Model of Information

We can see from the model that variables to try is equal to 3, indicating that a random selection of three explanatory variables was selected to test each possible split for each node in each tree within the forest.

SAS will grow 100 trees by default and select 60% of their sample when performing the bagging process (inbag fraction=0.6).

The prune fraction is equal to the default value: 0 which means that the tree is not to prune.

Leaf size specifies the smallest number of training observations that a new branch can have. The default value is 1.

The split criteria used in HPFOREST is the Gini index.

Missing value: Valid value since the target variable is not missing.

Number of Observations

Figure 3

The number of observations read from my data set is the same as the number of observations used (N= 600 for both number of observations).

For the baseline fit statistics output, the misclassification rate of the RF is 0.457. So, the algorithm misclassified 45.7% of the sample and the remaining 54.3% of the data were correctly classified (1-0.457=0.543).

Fit Statistics for 1 to 100 number of trees

PROC HPFOREST computes fit statistics for a sequence of forests that have an increasing number of trees. As the number of trees increases, the fit statistics usually improve. And this can be seen by the decreasing value of the average square error as the number of trees increases.

for 1 tree ASE (train)=0.1354, ASE (OOB)=0.211

for 10 trees ASE (train)=0.0810, ASE (OOB)=0.178

Figure 4: 10 first forests

for 90 trees ASE (train)=0.0716, ASE (OOB)=0.147

for 100 trees ASE (train)=0.0712, ASE (OOB)=0.146

Figure 5: 10 last forests

For the out of bag misclassification rate (OOB), we can notice that we get a near perfect prediction in the training samples as the number of trees grown gets closer to 100. When those same models are tested on the out of bag sample, the misclassification rate is around 17% (0.172 for 100 number of trees). OOB estimate is a convenient substitute for an estimate that is based on test data and is a less biased estimate of how the model will perform on future data.

Variable Importance Ranking

Figure 6: Loss Reduction Variable Importance

The final table in the output table for the random forest algorithms applied to this dataset which is the variable importance rankings. The number of rules column shows the number of splitting rules that use a variable. Each measure is computed twice, once on training data and once on the out of bag data. As with fit statistics, the out of bag estimates are less biased. The rows are sorted by the OOB Gini measure.

The variables are listed from highest importance to lowest importance in predicting the PEP value. Here we see that some of the most important variables in predicting the PEP for a client is the number of children, then whether he is married or not, whether he has a mortgage or not, whether he has a saving then current accounts, whether he has a car or not, his sex, his region, the income and age come at the end which means those are the least important values to decide determine his PEP value.

Conclusion

Random forests are a type of data mining algorithm that can select from among a large number of variables, those that are most important in determining the target or response variable to be explained. The target and the explanatory variables can be categorical or quantitative, or any combination. The forest of trees is used to rank the importance of variables in predicting the target. In this way, random forests are sometimes used as a data reduction technique, where variables are chosen in terms of their importance to be included in regression and other types of future statistical models.

爆發

0 則迴響

[sanaablog1]

Assignment 2:

Finding important features to predict the personal equity plan (PEP) in the banking system

Introduction:

A personal equity plan (PEP) was an investment plan introduced in the United Kingdom that encouraged people over the age of 18 to invest in British companies. Participants could invest in shares, authorized unit trusts, or investment trusts and receive both income and capital gains free of tax. The PEP was designed to encourage investment by individuals. Banks engage in data analysis related to Personal Equity Plans (PEPs) for various reasons. They use it to assess the risk associated with these investment plans. By examining historical performance, market trends, and individual investor behavior, banks can make informed decisions about offering PEPs to their clients.

In general, banks analyze PEP-related data to make informed investment decisions, comply with regulations, and tailor their offerings to customer needs. The goal is to provide equitable opportunities for investors while managing risks effectively. 

SAS Code

proc import out=mydata

    datafile="/home/u63879373/bank.csv"

    dbms=csv

    replace;

    getnames=YES;

PROC SORT; BY id;

ods graphics on;

PROC HPFOREST;

target PEP/level=nominal; /*target variable*/

input sex region married children car save_act current_act mortgage /level=nominal;

input age income /level=interval ;  

RUN;

Dataset

The dataset I used in this assignment contains information about customers in a bank. The Data analysis used will help the bank take know the important features that can affect the PEP of a client from the following features: age, sex, region, income, married, children, car, save_act, current_act and the mortgage.

Id: a unique identification number,

age: age of customer in years (numeric),

income: income of customer (numeric)

sex: MALE / FEMALE

married: is the customer married (YES/NO)

region: inner_city/rural/suburban/town

children: number of children (numeric)

car: does the customer own a car (YES/NO)

save_acct: does the customer have a saving account (YES/NO)

current_acct: does the customer have a current account (YES/NO)

mortgage: does the customer have a mortgage (YES/NO)

Figure1: dataset

Random Forest

Random forests are predictive models that allow for a data driven exploration of many explanatory variables in predicting a response or target variable. Random forests provide important scores for each explanatory variable and also allow you to evaluate any increases in correct classification with the growing of smaller and larger number of trees.

Random forest was used on the above dataset to evaluate the importance of a series of explanatory variables in predicting a binary, categorical response variable (PEP).

Following the import to import the dataset, I will include PROC HPFOREST. Next, I name my response, or target variable, PEP which is a nominal variable.

For the input statements I indicate the following:

Sex, region, married, children, car, save_act, current_act, and mortgage are nominal variables (categorical explanatory variables).

Age and income are interval variables (Quantitative explanatory variables).

Random Forest Analysis:

After running the code above we get the following results:

Model information

Figure2: Model of Information

We can see from the model that variables to try is equal to 3, indicating that a random selection of three explanatory variables was selected to test each possible split for each node in each tree within the forest.

SAS will grow 100 trees by default and select 60% of their sample when performing the bagging process (inbag fraction=0.6).

The prune fraction is equal to the default value: 0 which means that the tree is not to prune.

Leaf size specifies the smallest number of training observations that a new branch can have. The default value is 1.

The split criteria used in HPFOREST is the Gini index.

Missing value: Valid value since the target variable is not missing.

Number of Observations

Figure 3

The number of observations read from my data set is the same as the number of observations used (N= 600 for both number of observations).

For the baseline fit statistics output, the misclassification rate of the RF is 0.457. So, the algorithm misclassified 45.7% of the sample and the remaining 54.3% of the data were correctly classified (1-0.457=0.543).

Fit Statistics for 1 to 100 number of trees

PROC HPFOREST computes fit statistics for a sequence of forests that have an increasing number of trees. As the number of trees increases, the fit statistics usually improve. And this can be seen by the decreasing value of the average square error as the number of trees increases.

for 1 tree ASE (train)=0.1354, ASE (OOB)=0.211

for 10 trees ASE (train)=0.0810, ASE (OOB)=0.178

Figure 4: 10 first forests

for 90 trees ASE (train)=0.0716, ASE (OOB)=0.147

for 100 trees ASE (train)=0.0712, ASE (OOB)=0.146

Figure 5: 10 last forests

For the out of bag misclassification rate (OOB), we can notice that we get a near perfect prediction in the training samples as the number of trees grown gets closer to 100. When those same models are tested on the out of bag sample, the misclassification rate is around 17% (0.172 for 100 number of trees). OOB estimate is a convenient substitute for an estimate that is based on test data and is a less biased estimate of how the model will perform on future data.

Variable Importance Ranking

Figure 6: Loss Reduction Variable Importance

The final table in the output table for the random forest algorithms applied to this dataset which is the variable importance rankings. The number of rules column shows the number of splitting rules that use a variable. Each measure is computed twice, once on training data and once on the out of bag data. As with fit statistics, the out of bag estimates are less biased. The rows are sorted by the OOB Gini measure.

The variables are listed from highest importance to lowest importance in predicting the PEP value. Here we see that some of the most important variables in predicting the PEP for a client is the number of children, then whether he is married or not, whether he has a mortgage or not, whether he has a saving then current accounts, whether he has a car or not, his sex, his region, the income and age come at the end which means those are the least important values to decide determine his PEP value.

Conclusion

Random forests are a type of data mining algorithm that can select from among a large number of variables, those that are most important in determining the target or response variable to be explained. The target and the explanatory variables can be categorical or quantitative, or any combination. The forest of trees is used to rank the importance of variables in predicting the target. In this way, random forests are sometimes used as a data reduction technique, where variables are chosen in terms of their importance to be included in regression and other types of future statistical models.

[dasrupesh]

Nobody

There is nobody like you in this whole world and of course in the whole existence.You are amazingly unique individual.There can never be any person like you forever because nature doesn't make cabon copies.Nature makes look alike duplicates and twins but still they are different in characteristics and are unique in nature.There is no match of two persons in the whole world because world loves varieties. That is why nature makes variety of people because of diverseness and constant fluxing in moulding of people.Because of gene sequential mechanism, there are differentness among individuals.This is the world of inequals and uniques.There was nobody like you before, there is nobody like you at present and there is nobody like you in the future too in this existence.The mould is not the same for everyone when nature moulds you with her invisible hands.The unique mould moulds you to produce unique characteristics individuals.Because of different moulds, your life moulds differently.The grindmill grinds with equity but in a different way in grinding of everyone.Nobody is spared.There are no prejudices, misjudgment in grinding you.You always get what you deserve.The consequences of your wrong actions in your life lands yourself in grinder for paychecks.Your good acts doesn't fall in the grinder because grinder is for wrong acts.You must have experienced this phenomenon in your life someway or other because your experience is enough to prove to you but you can't prove and convince others about your weird or beautiful subjective experiences because experience is solely yours.Be careful in performing your actions because it has effects and consequences.Be vigilante of your actions and your life.Once done, it's done forever.Done act can not be undone.

Your intelligence doesn't match with others.Your level of thought may match with others and may not match with others.It all depends.Thought process match but thoughts don't match.The way may match coincidentically but the experiences on the way don't match because experiences are subjectives.Other's capabilities and potentials don't match with you in any way.Everyone have different capabilities and potentials which has it's own fine qualities.There is no matching of qualities.That is reason why there is nobody like you.You are different and inequal as everyone is inequal and different.There is a variation within species and among the species in the world.Tallented footballer may not be able to drive a Formula One car.The F1 driver may not be able to play cricket well enough.But, one can do it through acquiring skills through practice in certain field.But, there is a difference in performance in how you perform any task given to you by others or take on your own.Everyone performance is not the same.Not everyone scores the same marks in the school.There is variation is performance because of variation in intelligence, talents and skills.The fox can't climb the tree but leopard can.Tigress can't roar like the lion.Horse can't run fast like the cheetah.Salmon fish can't be intelligent like the dolphin.Dolphin can't be like the shark.This is variable world.This is not the world of constants.There are constants in the calculus but there are no constants in life because life itself is a variable entity.Life is a variable constant which varies constantly.If life would have been constant, then there would not have been changes in life which would have resulted in colourless life.But the fact is the life is colourful like the rainbow.If the earth would have been constant, then volcano would not have erupted and ĺava would not come have come out of the core through mantle.We all are living in non constant world and the variable life.

                The unique orientation leads to differentness and inequal individuals in the world.The being which resides inside you is made up of same elements as everybody else.The being is same.The pain of jabbing of pin unto you is same as everyone else.Your conscious nature is your beingness.Everybody has consciousness but the intensity and magnitude of your layers of consciousness are different.The conscious, subconscious, unconscious and super conscious are the layers of your being because your being is related to your consciousness.The different activities state your different spectrum of your consciousness which leads to different kind of individuals which is unique in character.That is the reason why nobody is like you and others are not like you.Different permutations and combinations of nucleotides in your gene results in different characteristics which ultimately leads to variation among individuals.There are variety of flowers In the garden which should be beautified and glorified.No any flower should be rejected in the garden because of lack of fragrance because life is not about rejections but life is all about acceptions.If you avoid and reject the beautiful flower, as all the flowers are beautiful then it is the betrayal of life.If it is so, then you are neglecting the life and to neglect the life is to neglect the world because world is life.You don't become nobody because of the neglection by some bunch of somebodies.

Email: dasrupesh22@gmail.com

[flamioannie]

TESA allows teachers the "opportunity to use a Tree Equity Score Analyzer (TESA) to guide local planting and activities... One Boston high school is using this tree equity tool to expand on STEM training and spark career prospects." -NAAEE

🌳

[existential-hedgehog]

I'm already over this year.

I have been to urgent care 3 times, 2x for my girlfriend and once for myself.

Now, today, we had a tree fall on our house. This tree was apparently not healthy and was not one of the trees we were warned about.

We now have to spend almost $7000 and potentially have to get a home equity loan to get our wall, attic, and brand new roof fixed

Also, we might have to go to the ER if the 2nd steroid shot doesn't work.

Oh, and did I mention that my girlfriend is now out of remission and has to get cancer treatment a second time???

I don't know who's keeping score but 2024 can go fuck itself

[bradfoe]

Asia | Best in class

Why are Vietnam’s schools so good?

It understands the value of education and manages its teachers well

Jun 29th 2023 | SINGAPORE

Ho chi minh, the founding father of Vietnam, was clear about the route to development. “For the sake of ten years’ benefit, we must plant trees. For the sake of a hundred years’ benefit, we must cultivate the people,” was a bromide he liked to trot out. Yet despite years of rapid economic growth, the country’s GDP per person is still only $3,760, lower than in its regional peers, Malaysia and Thailand, and barely enough to make the average Vietnamese feel well-nurtured. Still, Ho Chi Minh was alluding to a Chinese proverb extolling the benefits of education, and on that front Vietnam’s people can have few complaints.

Their children go through one of the best schooling systems in the world, a status reflected in outstanding performances in international assessments of reading, maths and science. The latest data from the World Bank show that, on aggregate learning scores, Vietnamese students outperform not only their counterparts in Malaysia and Thailand but also those in Britain and Canada, countries more than six times richer. Even in Vietnam itself, student scores do not exhibit the scale of inequality so common elsewhere between the genders and different regions. 

A child’s propensity to learn is the result of several factors—many of which begin at home with parents and the environment they grow up in. But that is not enough to explain Vietnam’s stellar performance. Its distinctive secret lies in the classroom: its children learn more at school, especially in the early years. 

Vietnamese schools, unlike those in other poor countries, have improved over time. A study published in 2022 by researchers at the Centre for Global Development, a think-tank based in Washington, dc, found that in 56 of 87developing countries the quality of education had deteriorated since the 1960s (see chart). Vietnam is one of a small minority of countries where schools have consistently bucked this trend. 

The biggest reason is the calibre of its teachers. Not that they are necessarily better qualified; they are simply more effective at teaching. One study comparing Indian with Vietnamese students attributes much of the difference in scores in mathematical tests to a gulf in teaching quality.

Vietnam’s teachers do their job well because they are well-managed. They receive frequent training and are given the freedom to make classes more engaging. To tackle regional inequality, those posted to remote areas are paid more. Most important, teacher assessment is based on the performance of their students. Those whose pupils do well are rewarded through presitigious “teacher excellence” titles.

Besides such carrots, a big stick is the threat of running foul of the ruling Communist Party. The party apparatus is obsessed with education. This percolates down to school level, where many head teachers are party members. 

The obsession has other useful effects. Provinces are required to spend 20% of their budgets on education, which has helped regional equity. That the party pays such close and relentless attention also ensures that policies are adjusted to update curriculums and teaching standards. Society at large shares the fixation. Vietnam’s families are committed to education because of its ingrained Confucianism, suggests Ngo Quang Vinh, a social-sector officer at the Asian Development Bank. He says that even poorer parents fork out for extra private tutoring. In cities, many seek schools where teachers have won “excellence in teaching” titles.

All this has reaped rich rewards. As schools have improved, so has Vietnam’s economy. But growth is testing the education system, suggests Phung Duc Tung, the director of the Mekong Development Research Institute, a think-tank in the capital, Hanoi. Firms increasingly want workers with more sophisticated skills, such as team management, that Vietnamese students are not trained for. Growth has also pulled in migrants to cities, overburdening urban schools. More and more teachers are forsaking education for higher-paying jobs in the private sector. To ensure Vietnam remains best-in-class, the government will have to tackle these trends. As Ho Chi Minh liked to remind people, cultivation requires constant attention. ■

This article appeared in the Asia section of the print edition of “the economist” under the headline "Best in class"

[detroitography]

Map: Tree Equity in Detroit

Map: Tree Equity in Detroit

Detroit needs a lot more trees. It’s no mystery that Detroit has been hit by deindustrialization and population loss on top of Dutch Elm disease leaving many trees either dead, dying, or left uncared for along city streets. I’ve previously looked at tree canopy disparities and posted about the dangers of heat islands and the absence of green space in a city. Recent City of Detroit efforts to…

[queerbrownvegan]

Does your neighborhood have a large density of trees or barely any? Tree Equity is essential when we talk about environmental justice. The term was coined by American Forests organization that looks into fighting for equitable tree coverage in all cities regardless of race, color, national origin, or income. It was found that low-income communities have lower density tree canopy rates in their neighborhoods compared to affluent communities. Tree equity relates to the environmental justice movement that advocates for clean air, water, and soil. According to the Arbor Day Foundation, in one year a mature tree will absorb more than 48 pounds of carbon dioxide from the atmosphere and release oxygen in exchange. Low-income families are struggling to cope with heat-related illnesses like asthma that are on the rise in these areas because of climate breakdown. The situation is exacerbated by the fact that fewer trees lead to increased levels of carbon dioxide thus resulting in reduced overall air quality. There are sites like tree equity score that help determine the level of quality of trees in your area. Trees are natural climate resilience tools that continue to be pushed out of the dominant economic model. Instead, there is a huge investment going into mechanical machines that absorb CO2 that are failing us rather than addressing how systemic racism prevents low-income communities from having access to green spaces. The loss of ecological knowledge for youth is being paired with a mechanistic framework that values profit. One famous example is that 88% of tree species in New York can be foraged for medicine and food. Well-maintained trees can minimize soil erosion during heavy rainfall, which wards off damage to the natural, built environment. If trees can co-exist, protect, feed, and nurture us, why are we not able to give back to them in a reciprocal way? That should be our goal.

[anniekoh]

elsewhere on the internet: carolina a. miranda

Oops auto-published the draft and without any explanation.

Carolina Miranda is a L.A. Times cultural reporter whose writing I consistently appreciate!

I assign one of my undergraduate classes a semester-long newspaper/news media journal, and I am curious if those who focused on the LA Times noticed if they gravitated to specific reporters!

In no particular order....

Who will fill Eli Broad’s philanthropic shoes? How about nobody (June 2021) https://www.latimes.com/entertainment-arts/story/2021-06-03/who-will-fill-eli-broads-philanthropic-shoes-how-about-nobody

And while we’re at it, let’s retire the outmoded idea that the most important factor in a city’s cultural landscape is the presence of some white knight bearing a checkbook and grandiose ideas about turning bulldozed Los Angeles neighborhoods into the Champs-Élysées (as Broad once described his vision for Bunker Hill).

LACMA draws an annual subsidy from L.A. County taxpayers of about $25 million — an amount that represents a quarter to a fifth of its annual budget. In Michigan, a $15 annual tax on residential properties valued at $150,000 in three counties goes toward funding the Detroit Institute of the Arts. In Colorado, a tiny portion of the sales tax (1 cent on every $10 in sales and use tax) is set aside to fund cultural and scientific institutions in a seven-county tax district around Denver.

Symbols of LA (July 2021) https://www.latimes.com/entertainment-arts/newsletter/2021-07-10/essential-arts-finding-surrender-enunciated-life-caam-essential-arts

My colleague Matt Pearce is known for spending July 4 tweeting photos of flaming palm trees with phrases like “DEATH TO PALM TREES. GOD BLESS AMERICA.” But in a new essay, he argues that perhaps it’s time to retire the palm tree as a symbol of Los Angeles. “Palm trees,” he writes, “when they are not being symbols, are kind of a problem to live with.” Besides functioning as July 4 kindling, the trees provide zero shade, fronds can strike passersby when they tumble, and worst yet, kill tree trimmers in some truly heinous work accidents.

...

— The Tree Equity Score map records the presence of shade trees in U.S. cities. Big takeaway: The poorest neighborhoods have 41% less coverage than the wealthiest ones, reports Linda Poon at CityLab. Plus, among major cities, L.A., San Diego and Houston have the highest need for trees.

Love of a Black planet: Artist April Bey’s Atlantica soars beyond Wakanda (Aug 2021) https://www.latimes.com/entertainment-arts/story/2021-08-18/april-bey-conjures-afrofuturist-world-atlantica-caam 

Newsletter: Designing resilient cities for the era of climate change (Sept 2021) https://www.latimes.com/entertainment-arts/newsletter/2021-09-04/essential-arts-urban-design-climate-change-essential-arts

This is Not a Gun (May 2021) https://www.latimes.com/entertainment-arts/newsletter/2021-05-29/this-is-not-a-gun-essential-arts

I’ve been thinking about Floyd a lot recently because of the anniversary — but also because of a small publication that came across my desk a few months ago and which I dip into regularly. “This Is Not a Gun” began as a simple art project by L.A.-based artist Cara Levine, then snowballed into a compelling series of collaborations between artists and activists that now have been recorded in book form. You can learn more about “This Is Not A Gun” on the project website (thisisnotagun.com). 

Every February, writer and cultural critic William Poundstone publishes a post he calls “Groundhog Day” on his perceptive and affable blog, Los Angeles County Museum on Fire, which looks at how Los Angeles has historically been described in the media.

[rjzimmerman]

Excerpt from this story from EcoWatch:

A new initiative is highlighting another branch of environmental racism and classicism: equal access to trees.

Conservation organization American Forests launched its first calculations for what it calls a Tree Equity Score June 22. This score evaluates cities and towns based on who has access to tree cover, and it found that low income and minority communities in the U.S. are less likely to be shaded than wealthier, majority-white ones. As the climate crisis makes urban heat waves more dangerous than ever, this is a serious environmental injustice.

"This country is denying life and death infrastructure to people based on income and race," American Forests President and CEO Jad Daley said in a press release. "That's morally insupportable."

According to American Forests' findings, U.S. neighborhoods with a majority of people of color have 33 percent fewer trees than majority white neighborhoods, while neighborhoods that are 90 percent or more low income have 41 percent fewer trees than neighborhoods where only 10 percent or less of the residents live in poverty.

This is a serious problem, because trees are an important natural solution to the "urban heat island effect," in which an urban environment can increase temperatures by five to seven degrees Fahrenheit during the day and as much as 22 degrees Fahrenheit at night. This effect already harms the health of vulnerable people, especially children and the elderly, and disproportionately impacts poor and minority communities. And the climate crisis is already making it worse.

Trees, however, can counteract this problem. The 100 feet surrounding a tree can be around three degrees Fahrenheit cooler than the rest of the city, The Guardian pointed out. Trees have other health benefits as well, since they remove particulate matter and can therefore reduce air pollution, another problem that disproportionately impacts low income and non-white communities.