dor_id: 11106

506.#.#.a: Público

590.#.#.d: Los artículos enviados a la revista "Atmósfera", se juzgan por medio de un proceso de revisión por pares

510.0.#.a: Consejo Nacional de Ciencia y Tecnología (CONACyT); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Scientific Electronic Library Online (SciELO); SCOPUS, Web Of Science (WoS); SCImago Journal Rank (SJR)

561.#.#.u: https://www.atmosfera.unam.mx/

650.#.4.x: Físico Matemáticas y Ciencias de la Tierra

336.#.#.b: article

336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://www.revistascca.unam.mx/atm/index.php/atm/index

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351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

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270.#.#.d: MX

270.1.#.d: México

590.#.#.b: Concentrador

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883.#.#.a: Revistas UNAM

590.#.#.a: Coordinación de Difusión Cultural

883.#.#.1: https://www.publicaciones.unam.mx/

883.#.#.q: Dirección General de Publicaciones y Fomento Editorial

850.#.#.a: Universidad Nacional Autónoma de México

856.4.0.u: https://www.revistascca.unam.mx/atm/index.php/atm/article/view/ATM.2017.30.02.05/46590

100.1.#.a: Schinko, Thomas; Bachner, Gabriel; Schleicher, Stefan; Steininger, Karl W.

524.#.#.a: Schinko, Thomas, et al. (2017). Modeling for insights not numbers: The long-term low-carbon transformation. Atmósfera; Vol. 30 No. 2, 2017; 137-161. Recuperado de https://repositorio.unam.mx/contenidos/11106

245.1.0.a: Modeling for insights not numbers: The long-term low-carbon transformation

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM

264.#.0.c: 2017

264.#.1.c: 2017-03-31

653.#.#.a: Low-carbon transformation; energy-economic modeling; climate-economic modeling; structured literature review

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC 4.0 Internacional, https://creativecommons.org/licenses/by-nc/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico editora@atmosfera.unam.mx

884.#.#.k: https://www.revistascca.unam.mx/atm/index.php/atm/article/view/ATM.2017.30.02.05

001.#.#.#: 022.oai:ojs.pkp.sfu.ca:article/52389

041.#.7.h: eng

520.3.#.a: Limiting global warming to prevent dangerous climate change requires drastically reducing global greenhouse gases emissions and a transformation towards a low-carbon society. Existing energy- and climate-economic modeling approaches that are informing policy and decision makers in shaping the future net-zero emissions society are increasingly seen with skepticism regarding their ability to forecast the long-term evolution of highly complex, nonlinear social-ecological systems. We present a structured review of state-of-the-art modeling approaches, focusing on their ability and limitations to develop and assess pathways towards a low-carbon society. We find that existing methodological approaches have some fundamental deficiencies that limit their potential to understand the subtleties of long-term low-carbon transformation processes. We suggest that a useful methodological framework has to move beyond current state of the art techniques and has to simultaneously fulfill the following requirements: (1) representation of an inherent dynamic analysis, describing and investigating explicitly the path between different states of system variables, (2) specification of details in the energy cascade, in particular the central role of functionalities and services that are provided by the interaction of energy flows and corresponding stock variables, (3) reliance on a clear distinction between structures of the sociotechnical energy system and socioeconomic mechanisms to develop it and (4) ability to evaluate pathways along societal criteria. To that end we propose the development of a versatile multi-purpose integrated modeling framework, building on the specific strengths of the various modeling approaches available while at the same time omitting their weaknesses. This paper identifies respective strengths and weaknesses to guide such development.

773.1.#.t: Atmósfera; Vol. 30 No. 2 (2017); 137-161

773.1.#.o: https://www.revistascca.unam.mx/atm/index.php/atm/index

046.#.#.j: 2021-10-20 00:00:00.000000

022.#.#.a: ISSN electrónico: 2395-8812; ISSN impreso: 0187-6236

310.#.#.a: Trimestral

300.#.#.a: Páginas: 137-161

264.#.1.b: Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM

doi: https://doi.org/10.20937/ATM.2017.30.02.05

handle: 0640e88a6841cea0

harvesting_date: 2023-06-20 16:00:00.0

856.#.0.q: application/pdf

last_modified: 2023-06-20 16:00:00

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No entro en nada

No entro en nada 2

Artículo

Modeling for insights not numbers: The long-term low-carbon transformation

Schinko, Thomas; Bachner, Gabriel; Schleicher, Stefan; Steininger, Karl W.

Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM, publicado en Atmósfera, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Entidad o dependencia
Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM
Revista
Repositorio
Contacto
Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

Cita

Schinko, Thomas, et al. (2017). Modeling for insights not numbers: The long-term low-carbon transformation. Atmósfera; Vol. 30 No. 2, 2017; 137-161. Recuperado de https://repositorio.unam.mx/contenidos/11106

Descripción del recurso

Autor(es)
Schinko, Thomas; Bachner, Gabriel; Schleicher, Stefan; Steininger, Karl W.
Tipo
Artículo de Investigación
Área del conocimiento
Físico Matemáticas y Ciencias de la Tierra
Título
Modeling for insights not numbers: The long-term low-carbon transformation
Fecha
2017-03-31
Resumen
Limiting global warming to prevent dangerous climate change requires drastically reducing global greenhouse gases emissions and a transformation towards a low-carbon society. Existing energy- and climate-economic modeling approaches that are informing policy and decision makers in shaping the future net-zero emissions society are increasingly seen with skepticism regarding their ability to forecast the long-term evolution of highly complex, nonlinear social-ecological systems. We present a structured review of state-of-the-art modeling approaches, focusing on their ability and limitations to develop and assess pathways towards a low-carbon society. We find that existing methodological approaches have some fundamental deficiencies that limit their potential to understand the subtleties of long-term low-carbon transformation processes. We suggest that a useful methodological framework has to move beyond current state of the art techniques and has to simultaneously fulfill the following requirements: (1) representation of an inherent dynamic analysis, describing and investigating explicitly the path between different states of system variables, (2) specification of details in the energy cascade, in particular the central role of functionalities and services that are provided by the interaction of energy flows and corresponding stock variables, (3) reliance on a clear distinction between structures of the sociotechnical energy system and socioeconomic mechanisms to develop it and (4) ability to evaluate pathways along societal criteria. To that end we propose the development of a versatile multi-purpose integrated modeling framework, building on the specific strengths of the various modeling approaches available while at the same time omitting their weaknesses. This paper identifies respective strengths and weaknesses to guide such development.
Tema
Low-carbon transformation; energy-economic modeling; climate-economic modeling; structured literature review
Idioma
eng
ISSN
ISSN electrónico: 2395-8812; ISSN impreso: 0187-6236

Enlaces