https://doi.org/10.14786/flr.v6i2

Mittelmeier, Jenna; Edwards, Rebecca L.; Davis, Sarah K.; Nguyen, Quan; Murphy, Victoria L.; Brummer, Leonie; Rienties, Bart

doi:10.14786/flr.v6i2.348

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…Hence, wrong strategic, operational or financial decisions could be made-at the detriment of the firm. Other research confirms this argument [160][161][162].…”

Section: Capabilities and Process-eco-innovationmentioning

confidence: 60%

Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies

Munodawafa

Johl

2019

Sustainability

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Increased greenhouse gas (GHG) emissions in the past decades have created concerns about the environment. To stymie global warming and the deterioration of the natural environment, global CO2 emissions need to reach approximately 1.3 tons per capita by 2050. However, in Malaysia, CO2 output per capita—driven by fossil fuel consumption and energy production—is expected to reach approximately 12.1 tons by the year 2020. GHG mitigation strategies are needed to address these challenges. Cleaner production, through eco-innovation, has the potential to arrest CO2 emissions and buttress sustainable development. However, the cleaner production process has been hampered by lack of complete data to support decision making. Therefore, using the resource-based view, a preliminary study consisting of energy and utility firms is undertaken to understand the impact of big data analytics towards eco-innovation. Linear regression through SPSS Version 24 reveals that big data analytics could become a strong predictor of eco-innovation. This paper concludes that information and data are key inputs, and big data technology provides firms the opportunity to obtain information, which could influence its production process—and possibly help arrest increasing CO2 emissions.

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“…Hence, wrong strategic, operational or financial decisions could be made-at the detriment of the firm. Other research confirms this argument [160][161][162].…”

Section: Capabilities and Process-eco-innovationmentioning

confidence: 60%

Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies

Munodawafa

Johl

2019

Sustainability

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“…The E3SM simulation data is available as part of the CMIP6 archive via the ESGF (Bader et al., 2019). The CAM simulation data used in this is available in a zenodo archive (Medeiros et al., 2023). The contents include the COSP outputs and ω 500 as monthly means.…”

Section: Data Availability Statementmentioning

confidence: 99%

Assessing Clouds Using Satellite Observations Through Three Generations of Global Atmosphere Models

Medeiros

Shaw

Kay

et al. 2023

Earth and Space Science

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Clouds are parameterized in climate models using quantities on the model grid‐scale to approximate the cloud cover and impact on radiation. Because of the complexity of processes involved with clouds, these parameterizations are one of the key challenges in climate modeling. Differences in parameterizations of clouds are among the main contributors to the spread in climate sensitivity across models. In this work, the clouds in three generations of an atmosphere model lineage are evaluated against satellite observations. Satellite simulators are used within the model to provide an appropriate comparison with individual satellite products. In some respects, especially the top‐of‐atmosphere cloud radiative effect, the models show generational improvements. The most recent generation, represented by two distinct branches of development, exhibits some regional regressions in the cloud representation; in particular the southern ocean shows a positive bias in cloud cover. The two branches of model development show how choices during model development, both structural and parametric, lead to different cloud climatologies. Several evaluation strategies are used to quantify the spatial errors in terms of the large‐scale circulation and the cloud structure. The Earth mover's distance is proposed as a useful error metric for the passive satellite data products that provide cloud‐top pressure‐optical depth histograms. The cloud errors identified here may contribute to the high climate sensitivity in the Community Earth System Model, version 2 and in the Energy Exascale Earth System Model, version 1.

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