green energy technologies
Environmental technology (envirotech), green technology (greentech) or clean technology (cleantech) is the application of one or more of environmental science, green chemistry, environmental monitoring and electronic devices to monitor, model and conserve the natural environment and resources, and to curb the negative impacts of human involvement. The term is also used to describe sustainable energy generation technologies such as photovoltaics, wind turbines, etc. Sustainable development is the core of environmental technologies. The term environmental technologies is also used to describe a class of electronic devices that can promote sustainable management of resources.
Language and Technology
Work at the Intersection of Language and Technology
Computational linguists help machines process human language. All the pioneering language-based technologies in use today — search engines, predictive text messaging, speech recognition, machine translation and voice-user interfaces — rely on the work of computational linguists.
Place yourself at the forefront of this dynamic field by earning a Master of Science in Computational Linguistics at the University of Washington.
Work in Emerging Technologies
Prepare yourself for an exciting career. Computational linguists are in demand in quickly evolving areas such as artificial intelligence and machine learning.
Gain Unique Knowledge
Our program is one of the few that combines the study of linguistics and natural language processing, giving you a unique depth of understanding and skillset for your career.
Get Valuable Experience
Gain hands-on experience through the program's rigorous internships. Our students have interned at some of the world's leading companies, including Amazon and Google.
New Master’s Degree in NLP 2020
Graduate studies in natural language processing at UCSC
NLP scientists in local industry and government
The Executive Director of the Graduate Programme
Adwait Ratnaparkhi has been appointed as executive director of the new master’s degree program in Natural Language Processing.
He is the former director of voice and natural language understanding R&D at Roku and has over 20 years of experience as a researcher and manager at companies such as IBM, Microsoft, Yahoo, and Nuance, and at startups such as 33Across, where he served as chief scientist. During his time at Roku he developed natural language understanding and conversational AI technologies.
Adwait holds a Ph.D. in computer science from the University of Pennsylvania and a B.S.E. in computer science from Princeton University.
Ratnaparkhi foresees a proliferation of smaller, customised voice assistants that will require NLP engineers and programmers.
interdisciplinary master’s in Social Data Science
Computational linguistics is a burgeoning field, and skills are in high-demand in many areas, including speech recognition, artificial intelligence, machine translation, big data, automated text analysis and web search. Brandeis offers three graduate degree programs for students interested in this field.
The two-year Master of Science in Computational Linguistics program is an accessible, intensive two-year curriculum for students who have a linguistics, language, computer science, mathematics, or science background, as well as students without prior study of computer science or linguistics.
The Five-Year BA/MS Program in Computational Linguistics program allows Brandeis BA students to complete the first-year coursework of the two-year Computational Linguistics MS degree during their undergraduate studies.
The Doctor of Philosophy (PhD) in Computer Science program allows students to study computational linguistics while pursuing their doctoral degree.
Graduates of the programs enjoy a very high placement rate in both industry jobs and PhD programs. Our alumni work or have worked in computational linguistics and natural language processing at companies ranging from Adobe, Amazon, Facebook, General Electric, Google, IBM and IBM Watson, and Intuit, to Athena Health, AVOKE, BBN, Basis Technology, Brigham and Women's Hospital, Callminer, Charles River Analytics, Crimson Hexagon, Luminoso, Linguamatics, The MITRE Corporation, Narrative Science, Partners Healthcare, QPID Health, Rakuten, Raytheon, SAP Labs, UFA Inc. and a range of start-ups in the greater metropolitan areas of Boston, New York City, Philadelphia, Chicago and California.
Master of Science in Computational Linguistics
The computational linguistics master's program at Rochester trains students to be conversant both in language analysis and computational techniques applied to natural language. The curriculum consists of courses in linguistics and computer science for a total of 32 credit hours.
Graduates from the computational linguistics program will be prepared for both further training at the PhD level in computer science and linguistics, as well as industry positions. A number companies such as Google, Amazon, Nuance, LexisNexis, and Oracle are searching for employees with advanced degrees in computational linguistics for positions ranging from speech recognition technology to improving translation systems to developing better models of language understanding.
Coursework
The curriculum consists of courses in linguistics and computer science, in roughly a 50/50 mix, for a total of 32 credit hours. Four courses (16 credits) are required in linguistics and four courses (16 credits) in computer science. The degree also requires a culminating special written project on a topic relevant to the student's interest and in consultation with individual advisors.
This program’s coursework can typically be completed in three full-time semesters. A fourth semester is for students to prepare their program’s final assignment, project, or thesis.
Linguistics Courses
Prerequisite
Students are required to have completed the following prerequisite course, or its equivalent.
LING 110: Introduction to Linguistic Analysis
Track Courses
Within linguistics, students will work with an advisor to create a “track” for their coursework in one of three areas:
Sound structure (LING 410, 427, 510)
Grammatical structure (LING 420, 460, 461, 462, 520)
Meaning (LING 425, 465, 466, 468, 525, 535)
Students will be encouraged to take LING 450 and LING 501 as it suits their programs.
Required
At least one of the following:
LING 410: Introduction to Language Sound Systems
LING 420: Introduction to Grammatical Systems
LING 425: Introduction to Semantic Analysis
Plus at least two from the following:
LING 427: Topics in Phonetics and Phonology
LING 450: Data Science for Linguistics
LING 460: Syntactic Theory
LING 461: Phrase Structure Grammar
LING 462: Topics in Experimental Syntax
LING 465: Formal Semantics
LING 466: Pragmatics
LING 468: Computational Semantics
LING 481: Statistical and Neural Computational Linguistics
LING 501: Linguistics Graduate Proseminar
LING 520: Syntax
LING 525: Graduate Semantics
LING 527: Topics in Phonetics and Phonology
LING 535: Formal Pragmatics
Computer Science Courses
Prerequisites
Students are required to have completed the following prerequisite courses, or its equivalents:
CSC 171: The Science of Programming
CSC 172: The Science of Data Structures
CSC 173: Computation and Formal Systems
MATH 150: Discrete Math
MATH 165: Linear Algebra with Differential Equations
Required
Students must take two of the following three courses for the MS in Computational Linguistics.
LING 424: Introduction to Computational Linguistics
CSC 447: Natural Language Processing
CSC 448: Statistical Speech and Language Processing
Plus at least two of the following:
CSC 440: Data Mining
CSC 442: Artificial Intelligence
CSC 444: Logical Foundations of Artificial Intelligence
CSC 446: Machine Learning
Introduction
What is computational linguistics? The Association for Computational Linguistics (ACL) describes computational linguistics as the scientific study of language from a computational perspective. Computational linguists provide computational models of various types of linguistic phenomena.
Computational linguistics (CL) combines resources from linguistics and computer science to discover how human language works.
Computational linguistics is a field of vital importance in the information age. Computational linguists create tools for important practical tasks such as machine translation, speech recognition, speech synthesis, information extraction from text, grammar checking, text mining and more.
Computational Linguistics Graduate Programs
The major schools in computational linguistics typically have a strong interdisciplinary culture with the linguistics department and the computer science department and with other related departments.
Where do you get a graduate degree with a specialization in computational linguistics? Some linguistics departments offer the specialization, however at many colleges and universities the computer science (CS) department or a related department actually offers the specialization. Some computer science departments don’t even mention a computational linguistics specialization at their website, however they actually have computer science graduate students specializing in computational linguistics along with faculty members performing research in the subject. Upon request the CS departments typically allow qualified graduate students to focus on CL.
Computational linguistic students study subjects such as semantics, computational semantics, syntax, models in cognitive science, natural language processing systems and applications, morphology, linguistic phonetics and phonology.
Students may also study sociolinguistics, psycholinguistics, corpus linguistics, machine learning, applied text analysis, grounded models of meaning, data-intensive computing for text analysis, and information retrieval.
During their journey computational linguistic students typically take computer programming courses as well as math and statistics courses. However, some general courses such as methods in computational linguistics teach computer programming at a level which provides students the skills to begin creating computer applications to address computational linguistics tasks. Some Ph.D. programs require students to have a proficiency in discrete mathematics or mathematical linguistics.
Ph.D. students specializing in CL in the computer science department can take courses such as operating systems, programming languages, analysis of algorithms, natural language processing, computation and formal systems, science of data structures, machine learning, artificial intelligence, and computer architecture.
Computational Linguistics Careers
Computational linguistics is the most commercially viable branch of linguistics; hundreds of companies in the United States work on computational linguistics. Computational linguists work for high tech companies, creating and testing models for improving or developing new software in areas such as speech recognition, grammar checkers, dictionary development and more. Computational linguists also work in the areas of computer-mediated language learning and artificial intelligence. They also work in research groups at universities and government research labs.
Some of the companies which employ computational linguists include:
Alelo
Apple
Expert System
Facebook
Google
Intel
Lingsoft
Lionbridge
Microsoft
North Side
Nuance
Oracle
SDL
Sensory
SRI STAR laboratory
Systran
Vantage Linguistics
VoiceWeb
Yahoo
Natural Language Processing
The computational linguistics and the natural language communities overlap. The methodologies of computational linguistics and natural language processing (NLP) are often related. Computational linguistics and natural language processing make use of formal training in linguistics, computer sciences and machine learning.
NLP allows computers to understand, analyze, and derive meaning from human language in an intelligent and useful way. NLP professionals organize and structure knowledge to perform tasks such as translation, relationship extraction, automatic summarization, sentiment analysis, text clustering and categorization, named entity recognition, text segmentation and speech recognition.
NPL systems, with their ability to analyze language for its meaning, have filled roles such as correcting grammar, automatically translating between languages, and converting speech to text.
Cognitive computing uses natural language processing in a variety of ways. Natural language processing provides a way for machines to communicate with people on conventional language-based terms, which makes NLP an important factor in cognitive computing.
Data scientist use natural language processing for log analysis of security models, risk management and regulatory compliance as well as price and demand forecasting.
Companies use NLP to improve the accuracy of documentation, improve the efficiency of documentation processes, and to identify the most pertinent information from large databases.
Natural language processing and text analytics are major factors in search and its numerous Internet-based applications. Companies use NLP in sentiment analysis of social media.
Contribute to one of the fastest-growing sectors!
Speech technologies have permeated modern life so much that we hardly even notice their presence, much less understand how they work. Whenever you dictate a message on your smartphone, ask Alexa the weather, use instant translation software, or learn a language using an app, you are using voice technologies. And with new Internet-of-Things applications on the horizon, smart spaces and the presence of Siri, Alexa, Cortana, Bixby and Google Home are poised to grow even further. Voice technologies are a multibillion-dollar industry with potential for unparalleled social and scientific impact.
Innovate, explore, create!
This programme is very hands-on. You will get your hands dirty working in teams making synthetic voices, speech recognizers, and more. You’ll even make your own voice-tech demo. People with scientific expertise in the domain of voice technology are in short supply. We aim to fix that. Starting with you! In this one-year programme, you will join cutting-edge scholars, professionals, and technologists working at the forefront of voice technology innovation.
Join the forefront of technological innovation!
Just as the smartphone ushered in a new wave of innovation, forever changing how we communicate, engage, navigate, and shop, so too is voice technology poised to fundamentally alter how we interact with our ubiquitous, interconnected devices.
A totally unique Master’s programme!
This Master’s programme is a one-of-its-kind. No other Master’s programme in continental Europe is dedicated exclusively to Voice Technology. No matter if your Bachelor’s degree is in linguistics, computer science, engineering, digital humanities, or something else altogether, if you are interested in voice technology and aren’t afraid of exploring new terrain, then this is the programme for you.
If you are interested in challenges relating to:
synthetic voices and speech recognition
the interplay between voice, language, speech and technology
tools to support lesser-resourced minoritized languages
radical innovation around voice forensics, including topics like accent recognition, intoxication detection, real-time speech pathology analysis, etc.
ethical issues relating to voice technology
then join us at the University of Groningen (Campus Fryslân) MSc Voice Technology.
Job prospects
Considering the numbers for the sectors ICT and linguistics, estimations are that people working in ICT and specifically application developers have remarkably positive career perspectives. These estimates are also reflected in the rapidly growing international market for voice assistants, smart speakers, and countless other IoT-connected devices enabled with voice technology.
Considering only the case for the Netherlands, after the introduction of Dutch-enabled smart speakers in 2018, the market for these products grew from 0% to 5% in under five months! The trend continues upward. Overall, the career perspectives for graduates from the Voice Technology MSc. are remarkably positive.
In designing this programme, we interview many Dutch speech technology companies. All of them have difficulties fulfilling local vacancies and remarked on the paucity of applicants with the requisite combination of linguistic knowledge, programming skills and experience with machine learning, all of which are core to the MSc Voice Technology.
Job examples
Speech Scientist
Speech Analyst
Research Engineer
Language Data Specialist
Voice Forensic Specialist
Entrepreneur
Various research and academic careers as a PhD student at several universities or academic speech technology labs in Europe and beyond!
Research
Culture, Language & Technology Flagship
The Culture, Language & Technology Flagship comprises an interdisciplinary team of doctoral researchers and lecturers who are dedicated to exploring how Human, Social and Behavioural Science-lead research can have a regional impact. The Flagship has two research themes:
Minoritized languages and multilingualism: Much scholarly activity at the CLT Flagship (including all PhD research) involves work on Frisian and Dutch minority cultures and languages alongside other minoritized cultures and languages.
Voice and speech technologies: Research is dedicated to voice and speech technologies. This theme includes not only technological components, but also social and cultural issues (e.g. how technology is used, by whom, and for what purposes)
Students in the MSc Voice Technology. may have the opportunity to collaborate on doctoral research, build up an international network and participate in periodic lectures, summer schools and events organized by the Culture, Language & Technology Flagship.
Computational linguistics graduate programs ranking guidelines:
We selected the graduate programs based on the quality of the program, types of courses provided, research opportunities, and faculty strength including research, awards, recognition and reputation. We also received advice from professors in the computational linguistics field.
Just as the smartphone ushered in a new wave of innovation, forever changing how we communicate, engage, navigate, and shop, so too is voice technology poised to fundamentally alter how we interact with our ubiquitous, interconnected devices
How do we cope with the information overload of modern media? What is the best way to collect data in a multilingual environment? Find out in this international Double Degree track.
The track in Language and Communication Technologies combines Theoretical Linguistics and Computer Science. You will study language technology in a multi-lingual setting.
The two-year training is part of the prestigious international Erasmus Mundus program. The first year you will start in Groningen. You will finish the program with a stay at one of our partner universities in the second year. After completing the track, you will receive two Master's degrees: a degree in Linguistics in Groningen and a second Master's degree depending on the partner university you chose to stay at.
The program consists of compulsory and optional courses. In this way, you can design the program o fit your interests. In addition, you will do a research project and write a Master's thesis.
Language and Communication Technologies is an Erasmus Mundus program.
Why study this program in Groningen?
Erasmus Mundus Master's Program.
A unique combination of theoretical linguistics and computer-science research in a multi-lingual setting.
A rapidly evolving area of study with excellent career opportunities, both in industry and academia.
Bachelor's diploma in a field related to Computer Science, Theoretical Linguistics, Artificial Intelligence.
The Association for Computational Linguistics describes computational linguistics as the scientific study of language from a computational perspective.
Computational linguistics (CL) combines resources from linguistics and computer science to discover how human language works. Computational linguists create tools for critical tasks such as machine translation, speech recognition, speech synthesis, grammar checking, and text mining.
Typically, computer science (CS) departments at colleges and universities offer computational linguistics as a specialization, though some linguistics departments also offer it. Some CS departments don't offer CL as a formal specialization, but qualified students can often work with faculty to create their own focus area.
Computational linguistics graduate students take computer programming, math, and statistics courses. They examine subjects such as semantics, computational semantics, natural language processing, models in cognitive science, and phonology.
A top-ranked private institution focused on technology, the Massachusetts Institute of Technology offers a doctorate in linguistics that lets students design their own focus area
At Stanford University, the Stanford Natural Language Processing (NLP) Group brings together faculty and graduate students in linguistics and computer science to advance the science of computer processing of human languages. Members of the group conduct research on computational linguistics,...
At Harvard University, graduate students can pursue a focus in computational linguistics through graduate programs in applied computation. The Institute for Applied Computational Science comprises graduate students and faculty focused on applied computational methods, including computational linguistics.
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The Departments of Linguistics and Computer Science and Engineering jointly participate in a Master of Science in Computational Linguistics. The mission of the program is to prepare students for a career in the Human Language Technologies industry. This program is on the STEM OPT extension list (CIP number 30.1801). If you are a current UB Linguistics PhD student interested in pursuing the MS as well, please view the PhD Applicants to MS page.
There are currently eight students in the MS program, two of which are also pursuing a PhD in Linguistics.
Student spotlight
MS graduate Xuejiao Chen accepted this Spring of 2020 a position as an assistant NLP Engineer at the Institute of Information Science of the China Electronics Technology Group Corporation.
MS graduate Soo Hyun Ryu has been accepted to the Psychology program at Michigan University, starting Fall 2020.
MS graduates Mengyang-Qiu and Xuejiao Chen have turned a term paper into a published conference paper: Qiu, M., Chen, X., Liu, M., Parvathala, K., Patil, A. & Park, J. (2019) "Improving precision of grammatical error correction with a cheat sheet", in Proceedings of the 14th Workshop on Innovative Use of NLP for Building Educational Applications, collocated with the 2019 ACL Conference.
MS student Soo Hyun Ryu will graduate in the Spring of 2019 and will be working as a researcher in the NLP*CL Lab at Korea Advanced Institute for Science and Technology (KAIST) as well as a Grammar Developer for Lionbridge.
MS student Soo Hyun Ryu presented a paper entitled "On the interaction between dependency frequency and thematic fit in sentence processingDownload pdf" at the 2019 Annual Meeting of the Society for Computation in Linguistics, NYC.
MS/PhD student Erika Bellingham completed an internship at Google, as an Analytical Linguist Intern (Intent Schema Team) from June 2018 to August 2018. She worked on natural language systems for the Google Assistant, and used linguistic analysis to improve Natural Language Understanding and Natural Language Generation systems.
MS/PhD student Hao Sun has graduated in the Spring of 2018, and was hired as an Artificial Intelligence Scientist by Astound, AI., a startup located in Menlo Park, California.
MS student Dianna Radpour took a leave during the Spring of 2018 to participate in an internship in the Reiken Institute (Tokyo, Japan). In the Fall of 2018 she graduated, and moved on to a PhD program at the University Colorado.
MS students are encouraged to seek internships they are interested in via Bullseye, or to participate in our local internship in the Natural Language Understanding Laboratory at the Department of Biomedical Sciences
The Stanford NLP Group is always on the lookout for budding new computational linguists. Stanford has a great program at the cutting edge of modern computational linguistics.
The best way to get a sense of what goes on in the NLP Group is to look at our research blog, publications, and students' and faculty's homepages. Our research centers around using probabilistic and other machine learning methods over rich linguistic representations in a variety of languages. The group is small, but productive and scientifically focused.
Deep learning areas
Scholarly articles for Deep learning areas
Deep learning - Goodfellow - Cited by 21585
… , yoshua bengio, and aaron courville: Deep learning - Heaton - Cited by 56
Deep Learning: Fundamentals, Theory and … - Huang - Cited by 26
6 areas of AI and Machine Learning to watch closelywww.kdnuggets.com › 2017/01 › 6-areas-ai-machine-l...
artificial-intelligence 1. Reinforcement learning (RL) · Applications · Principal Researchers · Companies ...
20 Deep Learning Applications in 2020 Across Industrieswww.mygreatlearning.com › blog › deep-learning-appl...
Feb 19, 2019 — Top Applications of Deep Learning Across Industries. Self Driving Cars. News Aggregation and Fraud News Detection. Natural Language Processing. Virtual Assistants. Entertainment. Visual Recognition. Fraud Detection. Healthcare.
Deep learning - Wikipediaen.wikipedia.org › wiki › Deep_learning
Deep-learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine ...
Feature learning · Deep belief network · Semi-supervised learning
14 Different Types of Learning in Machine Learningmachinelearningmastery.com › types-of-learning-in-ma...
Nov 11, 2019 — Machine learning is a large field of study that overlaps with and inherits ... Some machine learning algorithms are described as “supervised” machine ... Learning to Learn' is currently hottest research areas in deep learning.
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What is Deep Learning? - Machine Learning Masterymachinelearningmastery.com › what-is-deep-learning
Aug 16, 2019 — Discover exactly what deep learning is by hearing from a range of ... JASON I WANT TO WORK IN MEDICAL AREA OR IMPLEMENT IN TO ...
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Feb 29, 2016 — Unsupervised learning that would really kick ass · Introducing more reasoning abilities in our models · Natural language understanding and knowledge ...
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Top 10 Deep Learning Researchers Who Are Re-defining Its ...analyticsindiamag.com › top-10-deep-learning-research...
Oct 5, 2020 — In this article, we list ten deep learning researchers, in no particular order, who are re-defining the application areas of deep learning.
10 Companies Using Machine Learning in Cool Ways ...www.wordstream.com › machine-learning-applications
Aug 12, 2019 — What are some examples of machine learning and how it works in action? ... These days, it's probably easier to list areas of scientific R&D that ...
Best Machine Learning Applications in 2020 - Serokellserokell.io › blog › best-machine-learning-applications
Jul 23, 2020 — Machine learning allows scanning and digitizing documents in minutes. This solution can be used in universities, exam centers, museums, ...
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CS224n: Natural Language Processing with Deep Learning
Stanford / Winter 2020
Natural language processing (NLP) is a crucial part of artificial intelligence (AI), modeling how people share information. In recent years, deep learning approaches have obtained very high performance on many NLP tasks. In this course, students gain a thorough introduction to cutting-edge neural networks for NLP.
Assignment 1 (6%): Introduction to word vectors
Assignment 2 (12%): Derivatives and implementation of word2vec algorithm
Assignment 3 (12%): Dependency parsing and neural network foundations
Assignment 4 (12%): Neural Machine Translation with sequence-to-sequence and attention
Assignment 5 (12%): Neural Machine Translation with ConvNets and subword modeling
What is this course about?
Natural language processing (NLP) or computational linguistics is one of the most important technologies of the information age. Applications of NLP are everywhere because people communicate almost everything in language: web search, advertising, emails, customer service, language translation, virtual agents, medical reports, etc. In recent years, deep learning (or neural network) approaches have obtained very high performance across many different NLP tasks, using single end-to-end neural models that do not require traditional, task-specific feature engineering. In this course, students will gain a thorough introduction to cutting-edge research in Deep Learning for NLP. Through lectures, assignments and a final project, students will learn the necessary skills to design, implement, and understand their own neural network models. As piloted last year, CS224n will be taught using PyTorch this year.
Previous offerings
This course was formed in 2017 as a merger of the earlier CS224n (Natural Language Processing) and CS224d (Natural Language Processing with Deep Learning) courses. Below you can find archived websites and student project reports.
Proficiency in Python
All class assignments will be in Python (using NumPy and PyTorch). If you need to remind yourself of Python, or you're not very familiar with NumPy, you can come to the Python review session in week 1 (listed in the schedule). If you have a lot of programming experience but in a different language (e.g. C/C++/Matlab/Java/Javascript), you will probably be fine.
College Calculus, Linear Algebra (e.g. MATH 51, CME 100)
You should be comfortable taking (multivariable) derivatives and understanding matrix/vector notation and operations.
Basic Probability and Statistics (e.g. CS 109 or equivalent)
You should know basics of probabilities, gaussian distributions, mean, standard deviation, etc.
Foundations of Machine Learning (e.g. CS 221 or CS 229)
We will be formulating cost functions, taking derivatives and performing optimization with gradient descent. If you already have basic machine learning and/or deep learning knowledge, the course will be easier; however it is possible to take CS224n without it. There are many introductions to ML, in webpage, book, and video form. One approachable introduction is Hal Daumé’s in-progress A Course in Machine Learning. Reading the first 5 chapters of that book would be good background. Knowing the first 7 chapters would be even better!
Reference Texts
The following texts are useful, but none are required. All of them can be read free online.
Dan Jurafsky and James H. Martin. Speech and Language Processing (3rd ed. draft)
Jacob Eisenstein. Natural Language Processing
Yoav Goldberg. A Primer on Neural Network Models for Natural Language Processing
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning
Delip Rao and Brian McMahan. Natural Language Processing with PyTorch (requires Stanford login).
If you have no background in neural networks but would like to take the course anyway, you might well find one of these books helpful to give you more background:
Michael A. Nielsen. Neural Networks and Deep Learning
Eugene Charniak. Introduction to Deep Learning
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the US and Germany were planning to enter into a new climate and energy partnership
energy is the quantitative property
In physics, energy is the quantitative property that must be transferred to a body or physical system to perform work on the body, or to heat it. Energy is a conserved quantity; the law of conservation of energy states that energy can be converted in form, but not created or destroyed. The unit of measurement in the International System of Units (SI) of energy is the joule, which is the energy transferred to an object by the work of moving it a distance of one metre against a force of one newton.
Common forms of energy include the kinetic energy of a moving object, the potential energy stored by an object's position in a force field (gravitational, electric or magnetic), the elastic energy stored by stretching solid objects, the chemical energy released when a fuel burns, the radiant energy carried by light, and the thermal energy due to an object's temperature.
Mass and energy are closely related. Due to mass–energy equivalence, any object that has mass when stationary (called rest mass) also has an equivalent amount of energy whose form is called rest energy, and any additional energy (of any form) acquired by the object above that rest energy will increase the object's total mass just as it increases its total energy. For example, after heating an object, its increase in energy could be measured as a small increase in mass, with a sensitive enough scale.
Living organisms require energy to stay alive, such as the energy humans get from food. Human civilization requires energy to function, which it gets from energy resources such as fossil fuels, nuclear fuel, or renewable energy. The processes of Earth's climate and ecosystem are driven by the radiant energy Earth receives from the Sun and the geothermal energy contained within the earth.
A greenhouse gas
A greenhouse gas (GHG or GhG) is a gas that absorbs and emits radiant energy within the thermal infrared range, causing the greenhouse effect.[1] The primary greenhouse gases in Earth's atmosphere are water vapor (H
2O), carbon dioxide (CO
2), methane (CH
4), nitrous oxide (N
2O), and ozone (O3). Without greenhouse gases, the average temperature of Earth's surface would be about −18 °C (0 °F),[2] rather than the present average of 15 °C (59 °F).[3][4][5] The atmospheres of Venus, Mars and Titan also contain greenhouse gases.
Human activities since the beginning of the Industrial Revolution (around 1750) have increased the atmospheric concentration of carbon dioxide by almost 50%, from 280 ppm in 1750 to 419 ppm in 2021.[6] The last time the atmospheric concentration of carbon dioxide was this high was over 3 million years ago.[7] This increase has occurred despite the absorption of more than half of the emissions by various natural carbon sinks in the carbon cycle.[8][9]
At current greenhouse gas emission rates, temperatures could increase by 2 °C (3.6 °F), which the United Nations' Intergovernmental Panel on Climate Change (IPCC) says is the upper limit to avoid "dangerous" levels, by 2050.[10] The vast majority of anthropogenic carbon dioxide emissions come from combustion of fossil fuels, principally coal, petroleum (including oil) and natural gas, with additional contributions from deforestation and other changes in land use.[11][12]
President Joe Biden payments going out to families as part of the expanded child tax credit
President Joe Biden on Thursday called payments going out to families as part of the expanded child tax credit “historic,” saying they have the potential of lifting many families out of poverty.
Biden made his remarks at the White House late Thursday morning as payments reached some of the nearly 40 million families who qualified for the tax credits, affecting an estimated 65 million children.
“This is another step toward ending poverty in America,” Biden said. “This is our belief that the people who really need the tax cut are not the people at the top, but the people in the middle; the folks who are struggling.”
Biden compared the expanded child tax credit, approved under his American Rescue Plan passed earlier this year by Congress, to Social Security, which was passed in 1935.
“This has a chance to reduce child poverty in the same way Social Security reduced poverty for the elderly,” Biden said.
Vice President Kamala Harris, who spoke before Biden, said that providing the tax credits in monthly payments allows the program to have a more sustained impact on families.
“If the struggle to make ends meet is monthly, then the solution must be also,” Harris said. “The payments may be monthly, but the impact will be generational. This is the largest middle-class tax cut in a generation.
Qualifying families will receive a monthly payment of $300 per child under the age of 6 and $250 for children aged 6-17. The average payment is expected to be around $420.
Biden’s rescue plan expanded the maximum credit for children younger than 6 to $3,600 and to $3,000 for children 6 years old and older.
Parents earning an adjusted gross income under $75,000 and couples earning less than $150,000 will be eligible for the full credit. The amount of the payments phase out for parents who earn more, and individuals and couples who make more than $95,000 and $170,000 are not eligible.
Qualifying households can elect to opt out of the monthly payments and receive the full credit all at once when they file their 2021 taxes.
Most families who have filed their taxes or used the IRS’ non-filer tool in 2020 to receive their stimulus payments will not have to take action to receive their payments.
Those who don’t earn enough income to file taxes, but have eligible children can register to receive the payments through the IRS non-filer tool.
About 80% of eligible families will receive the monthly child tax credit by direct deposit if the IRS has their direct deposit information, and the agency will send paper checks and debit cards to some families.
Officials say future payments will go out on the 15th of every month through the end of 2021, unless the date falls on a weekend or holiday. The IRS says the remaining payments are scheduled to go out on Aug. 13, Sept. 15, Oct. 15, Nov. 15 and Dec. 15.
Energy technology
Energy technology is an interdisciplinary engineering science having to do with the efficient, safe, environmentally friendly, and economical extraction, conversion, transportation, storage, and use of energy, targeted towards yielding high efficiency whilst skirting side effects on humans, nature, and the environment. For people, energy is an overwhelming need, and as a scarce resource, it has been an underlying cause of political conflicts and wars. The gathering and use of energy resources can be harmful to local ecosystems and may have global outcomes. Energy is also the capacity to do work. We can get energy from food. Energy can be of different forms such as kinetic, potential, mechanical, heat, light etc. Energy is required for individuals and the whole society for lighting, heating, cooking, running, industries, operating transportation and so forth. Basically there are two types of energy depending on the source s they are; 1.Renewable Energy Sources 2.Non-Renewable Energy Sources
Printing Technology and Business process
Print on demand (POD) is a printing technology and business process in which book copies (or other documents) are not printed until the company receives an order, allowing prints of single or small quantities. While other industries established the build to order business model, "print on demand" could only develop after the beginning of digital printing because it was not economical to print single copies using traditional printing technology such as letterpress and offset printing.
Many traditional small presses have replaced their traditional printing equipment with POD equipment or contract their printing to POD service providers. Many academic publishers, including university presses, use POD services to maintain large backlists (lists of older publications); some use POD for all of their publications. Larger publishers may use POD in special circumstances, such as reprinting older, out-of-print titles, or for test marketing.
Predecessors
Before the introduction of digital printing technology, production of small numbers of publications had many limitations. Large print jobs were not a problem, but small numbers of printed pages were typically during the early 20th century produced using stencils and reproducing on a mimeograph or similar machine. These produced printed pages of inferior quality to a book, cheaply and reasonably fast. By about 1950, electrostatic copiers were available to make paper master plates for offset duplicating machines. From about 1960, copying onto plain paper became possible for photocopy machines to make multiple good-quality copies of a monochrome original.
In 1966, Frederik Pohl discussed in Galaxy Science Fiction "a proposal for high-speed facsimile machines which would produce a book to your order, anywhere in the world". As the magazine's editor, he said that "it, or something like it, is surely the shape of the publishing business some time in the future". As technology advanced, it became possible to store text in digital form – paper tape, punched cards readable by digital computer, magnetic mass storage, etc. – and to print on a teletypewriter, line printer or other computer printer, but the software and hardware to produce original good-quality printed colour text and graphics and to print small jobs fast and cheaply was unavailable.
Book publishing
Print on demand with digital technology is a way to print items for a fixed cost per copy, regardless of the size of the order. While the unit price of each physical copy is greater than with offset printing, the average cost is lower for very small print jobs, because setup costs are much greater for offset printing.
POD has other business benefits besides lesser costs (for small jobs):
Technical set-up is usually quicker than for offset printing.
Large inventories of a book or print material do not need to be kept in stock, reducing storage, handling costs, and inventory accounting costs.
There is little or no waste from unsold products.
Many publishers use POD for other printing needs other than books such as galley proof, catalogs and review copies.
These advantages reduce the risks associated with publishing books and prints and can result in increased choice for consumers. However, the reduced risks for the publisher can also mean that quality control is less rigorous than usual.
Other publishing
King and McGaw art prints are made on-demand at their warehouse in Newhaven, England.
Digital technology is ideally suited to publish small print jobs of posters (often as a single copy) when they are needed. The introduction of ultraviolet-curable inks and media for large-format inkjet printers has allowed artists, photographers and owners of image collections to take advantage of print on demand.
For example, UK art retailer King and McGaw fulfills many of its art print orders by printing on-demand rather than pre-printing and storing them until they are sold, requiring less space and reducing overheads to the business.[6] This was brought about after a fire destroyed £3 million worth of stock and damage to their warehouse. Other notable art retailers utilising POD include arthaus, Society6, TeePublic, and Redbubble.
Service providers
The introduction of POD technologies and business models has created a range of new book creation and publishing opportunities. There are three main categories of offerings.
Self-publishing authors
POD creates a new category of publishing (or printing) company that offers services, usually for a fee, directly to authors who wish to self-publish.
These services generally include printing and shipping each individual book ordered, handling royalties, and getting listings in online bookstores. The initial investment required for POD services is less than for offset printing. Other services may also be available, including formatting, proofreading, and editing, but such companies typically do not spend money for marketing, unlike conventional publishers. Such companies are suitable for authors prepared to design and promote their work themselves, with minimal assistance and at minimal cost. POD publishing gives authors editorial independence, speed to market, ability to revise content, and greater financial return per copy than royalties paid by conventional publishers. Self publishing also helps authors share their message with the world without waiting in line for a traditional publisher's approval.
POD enablement
While amateur/professional writers are targeted as early adopters by companies like Infinity Publishing and Trafford Publishing, there is an effort presently to make POD more mass-market. A class of companies like Lulu, Picaboo, Blurb, Peecho and QOOP have chosen to be "author-agnostic", attempting to serve a broad mass-market of ordinary citizens who may want to express, record and print keepsake copies of memories and personal writing (diaries, travelogues, wedding journals, baby books, family reunion reports etc.). Instead of tailoring themselves to the classic book format (at least 100 pages, mostly text, complex rules for copyright and royalties), these companies strive to make POD more mass-market by creating programs by which a range of different text and picture items can be produced as finished books. The management of copyrights and royalties is often less important for this market, as the books themselves have a small clientele (close family and friends, for instance).
The major photo storage services (e.g. Eastman Kodak's Ofoto and Shutterfly and Hewlett-Packard's Snapfish) have included the ability to produce picture books and calendars. However, they emphasize digital photography. The companies Blurb and Lulu apply this method to a greater volume of creative work (primarily text, as typed in personal weblogs) and include the capability to embed photographs and other media. QooP and Peecho assume the role of an infrastructure service provider, allowing any partner website to use its pre-designed payment and printing functions. The program Peecho provides an embeddable print button.
Publisher use
Print-on-demand services that offer printing and distributing services to publishing companies (instead of directly to self-publishing authors) are also growing in popularity within the industry. Many major publishers print on demand as a way to save money. It can become costly to print a book regularly that is going to sit on a bookshelf for more than a year before being purchased.
Print on demand allows texts to be revised and published rather more quickly. POD is environmentally friendly because there are only printing and shipping costs for actual sales. POD allows self-publishers to get their books out for little start-up costs.
Maintaining availability
Among traditional publishers, POD services can be used to make sure that books remain available when one print job has sold out, but another has not yet become available. This maintains the availability of older works, the estimated future sales of which may not be great enough to justify a further conventional print job. This can be useful for publishers with large backlists, such that sales for individual works may be few, but cumulative sales may be significant.
Managing uncertainty
Print on demand can be used to reduce risk when dealing with "surge" publications that are expected to have large sales but a brief sales life (such as biographies of minor celebrities, or event tie-ins): these publications represent good profitability but also great risk owing to the danger of inadvertently printing many more copies than are necessary, and the associated costs of maintaining excess inventory or pulping. POD allows a publisher to use cheaper conventional printing to produce enough copies to satisfy a pessimistic forecast of the publication sales, and then rely on POD to make up the difference.
POD offers advantages over conventional print production and distribution. POD service is not always easy to implement. Print providers and customers have to be willing to evaluate their business process with the flexibility and self-determination to change what is necessary.
Niche publications
Print on demand is also used to print and reprint "niche" books that may have a high retail price but limited sales opportunities, such as specialist academic works. An academic publisher may be expected to keep these specialist works in print even though the target market is almost saturated, making further conventional print jobs uneconomic.
The local history of a small community is well adapted to print on demand, as these books are invaluable to libraries, museums and archives in that small community but are limited in their marketability outside their home region. Public libraries which normally avoid print-on-demand tomes due to their lesser quality will readily make exceptions if content is appropriate for a local topic which cannot be addressed by more conventional means.
Many of the smallest small presses, often known as micro-presses because they have inconsequential profits, have become reliant on POD technology and ebooks. This is either because they serve such a small market that print jobs would be unprofitable or because they are too small to absorb much financial risk.
Variable formats
Print on demand also allows books to be printed in a variety of formats. This process, known as accessible publishing, allows books to be printed in a variety of larger type sizes and special formats for those with vision impairment or reading disabilities, as well as personalised typefaces and formats that suit an individual reader's needs.[8] This has been championed by a variety of new companies.
Economics
Profits from print-on-demand publishing are on a per-sale basis, and royalties vary depending on the method by which the item is sold. Greatest profits are usually generated from sales direct from a print-on-demand service's website or by the author buying copies from the service at a discount, as the publisher, and then selling them personally. Lesser royalties come from traditional bookshops and online retailers, both of which buy at high discount, although some POD companies allow the publisher or author to set their own discount level. Unless the publisher or author has fixed their discount rate, the greater the volume sold, the less the royalty becomes, as the retailer is able to buy at greater discount.
Because the per-unit cost is typically greater with POD than with a print job of thousands of copies, it is common for POD books to be more expensive than similar books made by conventional print jobs, especially if a book is produced exclusively with POD instead of using POD as a supplemental technology between print jobs.
Book stores order books through a wholesaler or distributor, usually at high discount of as much as 70%. Wholesalers obtain their books in two ways: either as a special order such that the book is ordered direct from a publisher when a book store requests a copy, or as stocked, which they keep in their own warehouse as part of their inventory. Stocked books are usually also available through "sale or return", meaning that the book store can return unsold stock for full credit as much as one year after the initial sale.
POD books are rarely if ever available on such terms because for the publishing provider it is considered too much of a risk. However, wholesalers monitor what works they are selling, and if authors promote their work successfully and achieve a reasonable number of orders from book stores or online retailers (who use the same wholesalers as the stores), then there is a reasonable chance of their work becoming available on such terms.
Although returnability lessens the risk for book stores, only a certain proportion of such stock can be returned. Non-returnability can make bookstores less enthusiastic about POD books.
Many print-on-demand publications are debut works;[citation needed] many bookstores are reluctant to risk an author's first, untested work without the endorsement of a commercial publisher.
Another issue is that these books are not available right away and take time to create (Friedlander). When a customer wants to purchase one of these books, they are less likely to follow through with the sale because they do not get the book that day. They are more likely to go home and order through another company like Amazon.
See also[edit]
Accessible publishing
Alternative media
Article processing charge
Author mill
Custom media
Dōjin
Dynamic publishing
Independent music
List of self-publishing companies
Offset printing
Online shopping
Outskirts press
Predatory open access publishing
Print on demand
Samizdat
Self Publish, Be Happy
Category:Self-published books
Self publishing
Sustainable energy
The use of energy is considered sustainable if it meets the needs of the present without compromising the needs of future generations. Definitions of sustainable energy typically include environmental aspects such as greenhouse gas emissions, and social and economic aspects such as energy poverty.
Meeting the world's need for energy in a sustainable way is one of the greatest challenges facing humanity in the 21st century. The global energy system, which is 85% based on fossil fuels, is responsible for over 70% of the greenhouse gas emissions that cause climate change. The burning of fossil fuels and biomass is a major contributor to air pollution, which causes an estimated 7 million deaths each year. More than 750 million people lack access to electricity and over 2.6 billion rely on polluting fuels such as wood or charcoal to cook.
Renewable energy sources such as wind, hydroelectric power, solar, and geothermal energy are generally far more sustainable than fossil fuel sources. However, some renewable energy projects, such as the clearing of forests for the production of biofuels, can cause severe environmental damage. The role of non-renewable energy sources has been controversial. For example, nuclear power is a low-carbon source and has a safety record comparable to wind and solar,[1] but its sustainability has been debated due to concerns about nuclear proliferation, radioactive waste and accidents. Switching from coal to natural gas has environmental benefits, but may lead to a delay in switching to more sustainable options. Carbon capture and storage technology can be built into power plants to remove their carbon dioxide emissions, but is expensive and has seldom been implemented.
Reducing greenhouse gas emissions to levels consistent with the Paris Agreement will require system-wide transformation of the way energy is produced, distributed, stored, and consumed. To accommodate larger shares of variable renewable energy, electrical grids require flexibility through infrastructure such as energy storage. A sustainable energy system is likely to see a shift towards far more use of electricity in sectors such as transport and heating, energy conservation, the use of hydrogen produced by renewables and from fossil fuels with Carbon capture and storage. Some technologies that are critical for eliminating energy-related greenhouse gas emissions are still in development.
Wind and solar energy sources generated 8.5% of worldwide electricity in 2019, a share that has grown rapidly. Costs of these energy sources, and of batteries, have fallen rapidly and are projected to continue falling due to innovation and economies of scale. Pathways exist to provide universal access to electricity and to clean cooking technologies in ways that are compatible with climate goals, while bringing major health and economic benefits to developing countries. Well-designed government policies that promote energy system transformation can lower greenhouse gas emissions and improve air quality simultaneously, and in many cases can also increase energy security. Policy approaches can include carbon-pricing and energy-specific policies such as renewable portfolio standards and phase-outs of fossil fuel subsidies.
Climate change
Climate change includes both global warming driven by human-induced emissions of greenhouse gases and the resulting large-scale shifts in weather patterns. Though there have been previous periods of climatic change, since the mid-20th century humans have had an unprecedented impact on Earth's climate system and caused change on a global scale.[2] The largest driver of warming is the emission of gases that create a greenhouse effect, of which more than 90% are carbon dioxide (CO 2) and methane.[3] Fossil fuel burning (coal, oil, and natural gas) for energy consumption is the main source of these emissions, with additional contributions from agriculture, deforestation, and manufacturing.[4] The human cause of climate change is not disputed by any scientific body of national or international standing.[5] Temperature rise is accelerated or tempered by climate feedbacks, such as loss of sunlight-reflecting snow and ice cover, increased water vapour (a greenhouse gas itself), and changes to land and ocean carbon sinks. Temperature rise on land is about twice the global average increase, leading to desert expansion and more common heat waves and wildfires.[6] Temperature rise is also amplified in the Arctic, where it has contributed to melting permafrost, glacial retreat and sea ice loss.[7] Warmer temperatures are increasing rates of evaporation, causing more intense storms and weather extremes.[8] Impacts on ecosystems include the relocation or extinction of many species as their environment changes, most immediately in coral reefs, mountains, and the Arctic.[9] Climate change threatens people with food insecurity, water scarcity, flooding, infectious diseases, extreme heat, economic losses, and displacement. These human impacts have led the World Health Organization to call climate change the greatest threat to global health in the 21st century.[10] Even if efforts to minimise future warming are successful, some effects will continue for centuries, including rising sea levels, rising ocean temperatures, and ocean acidification.[11] Energy flows between space, the atmosphere, and Earth's surface. Current greenhouse gas levels are causing a radiative imbalance of about 0.9 W/m2.[12] Many of these impacts are already felt at the current level of warming, which is about 1.2 °C (2.2 °F).[13] [14] The Intergovernmental Panel on Climate Change (IPCC) has issued a series of reports that project significant increases in these impacts as warming continues to 1.5 °C (2.7 °F) and beyond.[15] Additional warming also increases the risk of triggering critical thresholds called tipping points.[16] Responding to these impacts involves both mitigation and adaptation.[17] Mitigation – limiting climate change – consists of reducing greenhouse gas emissions and removing them from the atmosphere.[17] Methods to achieve this include the development and deployment of low-carbon energy sources such as wind and solar, a phase-out of coal, enhanced energy efficiency, and forest preservation. Adaptation consists of adjusting to actual or expected climate,[17] such as through improved coastline protection, better disaster management, assisted colonisation, and the development of more resistant crops. Adaptation alone cannot avert the risk of "severe, widespread and irreversible" impacts.[18] Under the 2015 Paris Agreement, nations collectively agreed to keep warming "well under 2.0 °C (3.6 °F)" through mitigation efforts. However, with pledges made under the Agreement, global warming would still reach about 2.8 °C (5.0 °F) by the end of the century.[19] Limiting warming to 1.5 °C (2.7 °F) would require halving emissions by 2030 and achieving near-zero emissions by 2050.[20]
The Paris Agreement
The Paris Agreement (French: Accord de Paris) is an agreement within the United Nations Framework Convention on Climate Change (UNFCCC) on climate change mitigation, adaptation, and finance, signed in 2016. The agreement was negotiated by 196 parties at the 21st Conference of the Parties of the UNFCCC in Le Bourget, near Paris, France, and adopted by consensus on 12 December 2015. As of July 2021, 191 members of the UNFCCC are parties to the agreement. Of the six UNFCCC member states which have not ratified the agreement, the only major emitters are Iran, Turkey, and Iraq (though the president has approved that country's accession). The United States withdrew from the agreement in 2020, but rejoined in 2021.
The Paris Agreement was opened for signature on 22 April 2016 (Earth Day) at a ceremony in New York. After the European Union ratified the agreement in October 2016, there were enough countries that had ratified the agreement that produce enough of the world's greenhouse gases for the agreement to enter into force on 4 November 2016.
The Paris Agreement's long-term temperature goal is to keep the rise in global average temperature to well below 2 °C (3.6 °F) above pre-industrial levels, and to pursue efforts to limit the increase to 1.5 °C (2.7 °F), recognizing that this would substantially reduce the impacts of climate change. This should be done by reducing emissions as soon as possible and achieving a net-zero emissions in the second half of the 21st century. It also aims to increase the ability of parties to adapt to the impacts of climate change, and mobilise sufficient finance. Under the Agreement, each country must determine, plan, and regularly report on its contributions. No mechanism forces a country to set specific emissions targets, but each target should go beyond previously set targets. In contrast to the 1997 Kyoto Protocol, the distinction between developed and developing countries is blurred, so that the latter also have to submit plans for emission reductions.
The Agreement was lauded by world leaders, but criticised as insufficiently binding by some environmentalists and analysts. There is debate about the effectiveness of the Agreement. While current pledges under the Paris Agreement are insufficient for reaching the set temperature goals, there is a mechanism of increased ambition. The Paris Agreement has been successfully used in climate litigation forcing countries and an oil company to strengthen climate action
Carbon neutrality
Carbon neutrality refers to achieving net-zero carbon dioxide emissions. This can be done by balancing emissions of carbon dioxide with its removal (often through carbon offsetting) or by eliminating emissions from society (the transition to the "post-carbon economy").[1] It is used in the context of carbon dioxide-releasing processes associated with transportation, energy production, agriculture, and industry.
Although the term "carbon neutral" is used, a carbon footprint also includes other greenhouse gases, usually carbon-based, measured in terms of their carbon dioxide equivalence. The term climate-neutral reflects the broader inclusiveness of other greenhouse gases in climate change, even if CO2 is the most abundant. The term "net zero" is increasingly used to describe a broader and more comprehensive commitment to decarbonization and climate action, moving beyond carbon neutrality by including more activities under the scope of indirect emissions, and often including a science-based target on emissions reduction, as opposed to relying solely on offsetting.
A carbon footprint
A carbon footprint is the total greenhouse gas (GHG) emissions caused by an individual, event, organization, service, place or product, expressed as carbon dioxide equivalent.[1] Greenhouse gases, including the carbon-containing gases carbon dioxide and methane, can be emitted through the burning of fossil fuels, land clearance and the production and consumption of food, manufactured goods, materials, wood, roads, buildings, transportation and other services.[2] The term was popularized by a $250 million advertising campaign by the oil and gas company BP in an attempt to move public attention away from restricting the activities of fossil fuel companies and onto individual responsibility for solving climate change.[3]
In most cases, the total carbon footprint cannot be calculated exactly because of inadequate knowledge of and data about the complex interactions between contributing processes, including the influence of natural processes that store or release carbon dioxide. For this reason, Wright, Kemp, and Williams proposed the following definition of a carbon footprint:
A measure of the total amount of carbon dioxide (CO2) and methane (CH4) emissions of a defined population, system or activity, considering all relevant sources, sinks and storage within the spatial and temporal boundary of the population, system or activity of interest. Calculated as carbon dioxide equivalent using the relevant 100-year global warming potential (GWP100).[4]
The global average annual carbon footprint per person in 2014 was about 5 tonnes CO2eq.[5]
The United Nations Framework Convention on Climate Change
The United Nations Framework Convention on Climate Change (UNFCCC) established an international environmental treaty to combat "dangerous human interference with the climate system", in part by stabilizing greenhouse gas concentrations in the atmosphere.[1] It was signed by 154 states at the United Nations Conference on Environment and Development (UNCED), informally known as the Earth Summit, held in Rio de Janeiro from 3 to 14 June 1992. It established a Secretariat headquartered in Bonn and entered into force on 21 March 1994.[2] The treaty called for ongoing scientific research and regular meetings, negotiations, and future policy agreements designed to allow ecosystems to adapt naturally to climate change, to ensure that food production is not threatened and to enable economic development to proceed in a sustainable manner.[2][3]
The Kyoto Protocol, which was signed in 1997 and ran from 2005 to 2020, was the first implementation of measures under the UNFCCC. The Kyoto Protocol was superseded by the Paris Agreement, which entered into force in 2016.[4] As of 2020, the UNFCCC has 197 signatory parties. Its supreme decision-making body, the Conference of the Parties (COP), meets annually to assess progress in dealing with climate change.[5][6]
The treaty established different responsibilities for three categories of signatory states. These categories are developed countries, developed countries with special financial responsibilities, and developing countries.[3] The developed countries, also called Annex 1 countries, originally consisted of 38 states, 13 of which were Eastern European states in transition to democracy and market economies, and the European Union. All belong to the Organisation for Economic Co-operation and Development (OECD). Annex 1 countries are called upon to adopt national policies and take corresponding measures on the mitigation of climate change by limiting their anthropogenic emissions of greenhouse gases as well as to report on steps adopted with the aim of returning individually or jointly to their 1990 emissions levels.[3] The developed countries with special financial responsibilities are also called Annex II countries. They include all of the Annex I countries with the exception of those in transition to democracy and market economies. Annex II countries are called upon to provide new and additional financial resources to meet the costs incurred by developing countries in complying with their obligation to produce national inventories of their emissions by sources and their removals by sinks for all greenhouse gases not controlled by the Montreal Protocol.[3] The developing countries are then required to submit their inventories to the UNFCCC Secretariat.[3] Because key signatory states are not adhering to their individual commitments, the UNFCCC has been criticized as being unsuccessful in reducing the emission of carbon dioxide since its adoption.[7]
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