Hasil untuk "Environmental sciences"

E. Kozliak, J. Páca

C. Raymond, I. Fazey, M. Reed et al.

Y. Ju-Nam, J. Lead

S. Shokralla, Jennifer L. Spall, J. Gibson et al.

D. Guston

R. Michener, K. Lajtha

D. Pimentel, S. Mcnair, J. Janecka et al.

P. Groffman, J. Baron, T. Blett et al.

D. Sarewitz

K. Hipel, Ian McLeod

Elizabeth R Desombre

K. O’Brien

M. Roveri, R. Flecker, W. Krijgsman et al.

S. Fang, Lida Xu, Yunqiang Zhu et al.

C. Kirchhoff, M. Lemos, S. Dessai

G. Laniak, Gabe P. Olchin, J. Goodall et al.

Integrated environmental modeling (IEM) is inspired by modern environmental problems, decisions, and policies and enabled by transdisciplinary science and computer capabilities that allow the environment to be considered in a holistic way. The problems are characterized by the extent of the environmental system involved, dynamic and interdependent nature of stressors and their impacts, diversity of stakeholders, and integration of social, economic, and environmental considerations. IEM provides a science-based structure to develop and organize relevant knowledge and information and apply it to explain, explore, and predict the behavior of environmental systems in response to human and natural sources of stress. During the past several years a number of workshops were held that brought IEM practitioners together to share experiences and discuss future needs and directions. In this paper we organize and present the results of these discussions. IEM is presented as a landscape containing four interdependent elements: applications, science, technology, and community. The elements are described from the perspective of their role in the landscape, current practices, and challenges that must be addressed. Workshop participants envision a global scale IEM community that leverages modern technologies to streamline the movement of science-based knowledge from its sources in research, through its organization into databases and models, to its integration and application for problem solving purposes. Achieving this vision will require that the global community of IEM stakeholders transcend social, and organizational boundaries and pursue greater levels of collaboration. Among the highest priorities for community action are the development of standards for publishing IEM data and models in forms suitable for automated discovery, access, and integration; education of the next generation of environmental stakeholders, with a focus on transdisciplinary research, development, and decision making; and providing a web-based platform for community interactions (e.g., continuous virtual workshops).

Haixin Chang, Hongkai Wu

A. Skidmore, N. Pettorelli, N. Coops et al.

François Rottenberg, Thomas Feys, Liesbet Van der Perre

The technology industry promotes artificial intelligence (AI) as a key enabler to solve a vast number of problems, including the environmental crisis. However, when looking at the emissions of datacenters from worldwide service providers, we observe a rapid increase aligned with the advent of AI. Some actors justify it by claiming that the increase of emissions for digital infrastructures is acceptable as it could help the decarbonization of other sectors, e.g., videoconference tools instead of taking the plane for a meeting abroad, or using AI to optimize and reduce energy consumption. With such conflicting claims and ambitions, it is unclear how the net environmental impact of AI could be quantified. The answer is prone to uncertainty for different reasons, among others: lack of transparency, interference with market expectations, lack of standardized methodology for quantifying direct and indirect impact, and the quick evolutions of models and their requirements. This report provides answers and clarifications to these different elements. Firstly, we consider the direct environmental impact of AI from a top-down approach, starting from general information and communication technologies (ICT) and then zooming in on data centers and the different phases of AI development and deployment. Secondly, a framework is introduced on how to assess both the direct and indirect impact of AI. Finally, we finish with good practices and what we can do to reduce AI impact.

Hidekazu Yoshioka, Kunihiko Hamagami

Focusing on the assessment of benthic algae blooms that decay subexponentially, we propose a tractable (solvable in a closed form) and well-defined (that does not diverge) environmental index for the impact assessment of long-memory processes under model uncertainties. Our target system generates long memory through an infinite superposition of multiscale processes. The sensitivity of the environmental index can be controlled by the degree of model uncertainty in terms of the relative entropy and nonexponential discount; hence, we apply a long-memory discount to evaluate long-memory processes. In our framework, the evaluation of the environmental index is reduced to finding a proper solution to an infinite-dimensional extended Hamilton-Jacobi-Bellman system. We can solve this system under sufficient conditions for the unique existence of sufficiently regular solutions, and numerically handle them by using a quantization technique. Finally, we present a demonstrative application of the proposed framework to benthic algae population dynamics in river environments based on a laboratorial experiment. This paper offers a tractable framework towards the assessment of persistent environmental phenomena.