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Topic · Water Science and Technology

Sustainability, Environment, and Optimization Algorithms

Sustainability, Environment, and Optimization Algorithms is a research topic within Water Science and Technology. Science Explorer counts 517 research works in it since 1953. 8.1% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on integrated water resources management, sustainability, and the impact of climate change on water availability. It explores the use of genetic algorithms and multi-agent systems in optimizing resource management, as well as the intersection of healthcare technology and environmental sustainability. The cluster also delves into land use change, biodiversity conservation, and the accounting of natural resources, providing valuable insights for environmental policy.

  • Water Resources Management
  • Sustainability
  • Climate Change
  • Genetic Algorithm
  • Multi-Agent System
  • Healthcare Technology
  • Land Use Change
  • Biodiversity Conservation
  • Natural Resource Accounting
  • Environmental Policy
Research works
517
fractional, since 1953
In the world top 10%
42
per year above
Top-10% rate
8.1%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+8%
the tick is no change

Which countries lead Sustainability, Environment, and Optimization Algorithms research?

By volume, Germany and China publish the most (61 and 20 works in 2022–2025).

By volume, 2022–2025

  1. 1 Germany 61 works
  2. 2 China 20 works
  3. 3 Austria 5 works
  4. 4 Switzerland 3 works
  5. 5 United States 3 works
  6. 6 Netherlands 2 works
  7. 7 Azerbaijan 2 works
  8. 8 India 1 works
  9. 9 Spain 1 works
  10. 10 Argentina 1 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

Germany: 61.3%China: 20.5%Austria: 5.1%Switzerland: 3.1%6 others listed: 10.0%61%largest
Germany61 · 61.3%China20 · 20.5%Austria5 · 5.1%Switzerland3 · 3.1%6 others listed10 · 10.0%

Shares of the rows listed above, not of the whole node.

Which institutions lead Sustainability, Environment, and Optimization Algorithms research?

By volume in 2022–2025, University of Duisburg-Essen publishes the most Sustainability, Environment, and Optimization Algorithms research, followed by TU Dortmund University and Ruhr University Bochum.

By volume, 2022–2025

  1. 1 University of Duisburg-EssenGermany 2 works
  2. 2 TU Dortmund UniversityGermany 2 works
  3. 3 Ruhr University BochumGermany 2 works
  4. 4 Humboldt-Universität zu BerlinGermany 2 works
  5. 5 München KlinikGermany 2 works
  6. 6 Nakhchivan State UniversityAzerbaijan 1 works
  7. 7 Hochschule HannoverGermany 1 works
  8. 8 Bochum University of Applied SciencesGermany 1 works
  9. 9 Bielefeld UniversityGermany 1 works
  10. 10 Leibniz University HannoverGermany 1 works

Where is Sustainability, Environment, and Optimization Algorithms research done?

The largest centres of Sustainability, Environment, and Optimization Algorithms research in 2022–2025 are Berlin (Germany), Vienna (Austria), Munich (Germany) and Bochum (Germany).

Largest cities, 2022–2025

  1. 1 Berlin Germany 8 works
  2. 2 Vienna Austria 4 works
  3. 3 Munich Germany 4 works
  4. 4 Bochum Germany 3 works
  5. 5 Essen Germany 3 works
  6. 6 Dortmund Germany 3 works
  7. 7 Hanover Germany 3 works
  8. 8 Beijing China 3 works
  9. 9 Shenyang China 2 works
  10. 10 Wuhan China 2 works
See Sustainability, Environment, and Optimization Algorithms on the map

Is Sustainability, Environment, and Optimization Algorithms research growing?

Output in 2018–2022 was 8% higher than in 2013–2017, peaking in 2026. The fastest-growing topics are Sustainability, Environment, and Optimization Algorithms.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.