Analysis of development trends and structure of «green» investments in the world
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
The analysis of development trends and the structure of «green» investments of the countries of the world is a relevant topic in the modern world, since the «green» economy and sustainable development are among the main priorities of many states. In the scientific work, the main trends in the development of «green» investments in the world as a whole and separately in selected countries were analyzed, the regularities and peculiarities of their structure were revealed. The main focus of the research is on countries with a high level of development, dense population, as well as economies that are actively developing and that have serious problems with energy supply. Among such countries, we can highlight the USA, China, Japan, Germany, France, Great Britain, Canada, Italy, Spain, Australia, Brazil and others. It was determined that the dynamics of «green» investments in the world for the period 2011–2021 shows significant growth. In 2012, the total amount of «green» investment in the world was 254 billion USD, and in 2021 it will reach 2.5 trillion USD. The lion's share of «green» investments in the world is directed to the field of renewable energy, the second place is occupied by the production of electric cars, the third is energy efficiency, and the fourth is the protection of water resources. The priority directions of «green» investments are highlighted, in particular, renewable energy sources, energy-efficient technologies, environmentally friendly production, etc. Based on the results of the research, the main laws and trends of the development of «green» investments in different countries of the world were determined, as well as the peculiarities of their structure were revealed. Factors affecting the development of the green economy and «green» investments in the countries of the world, such as financial incentives, state regulation, technological progress, and increased public awareness of environmental issues, are also analyzed. The growing dynamics of the implementation of nature-based solutions at the global level was noted. The results of the research can be useful for developing strategies for the development of the «green» economy and «green» investments in different countries of the world, as well as for determining priorities in the development of environmentally friendly technologies and productions.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle