How Can Big Data Complement Expert Analysis? A Value Chain Case Study

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.

Section: Overview On the State Of Big Data Analytics And Eco-innovationmentioning

confidence: 99%

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

Munodawafa

Johl

2019

“…Although initially developed to help understand the value creation process at the firm level, the value chain concept is now also used as a tool for understanding value creation in industries and countries [14,15]. At the industry level, a value chain analysis provides a comprehensive view of an industry, thereby supporting strategic and technology planning for incumbents and new entrants, as well as policy making at a higher level [16].…”

Section: Analytical Framework and Related Literaturementioning

confidence: 99%

A Systematic Literature Review of the Solar Photovoltaic Value Chain for a Circular Economy

Franco

Groesser

2021

As the solar photovoltaic market booms, so will the volume of photovoltaic (PV) systems entering the waste stream. The same is forecast for lithium-ion batteries from electric vehicles, which at the end of their automotive life can be given a second life by serving as stationary energy storage units for renewable energy sources, including solar PV. The main objective of this paper is to systematically review the “state-of-the-art” research on the solar PV value chain (i.e., from product design to product end-of-life), including its main stages, processes, and stakeholder relationships, in order to identify areas along the value chain where circular strategies could be implemented, thereby advancing the transition of the PV industry towards circularity. To achieve this goal, we conducted a systematic literature review of 148 peer-reviewed articles, published in English between 2000 and 2020. Results show the PV value chain has been studied from a forward flow supply chain perspective and mostly from a technological point of view, with little regard for circular design, circular business models, and PV reuse. This article thus takes an integrated value chain perspective, introduces some of the barriers to circularity that industry players face, and argues that these barriers represent future opportunities for incumbent and new entrants to innovate within a circular PV industry.

“…The available studies offer, on the one hand, experimentations in the use of BD [6], social media, in particular, to generate Value, without involving decision-makers [7]. On the other hand, several previous works of research have pointed out that BD has the potential to generate Value for business and also to tackle societal challenges [8][9][10]. Further to this, an issue that has been overlooked in previous studies is that this generation of Value is linked to the way BD is used by people working inside organizations and governments.…”

Section: Introductionmentioning

confidence: 99%

The Missing Variable in Big Data for Social Sciences: The Decision-Maker

Arnaboldi

2018

The value of big data for social sciences and social impact is professed to be high. This potential value is related, however, to the capacity of using extracted information in decision-making. In all of this, one important point has been overlooked: when “humans” retain a role in the decision-making process, the value of information is no longer an objective feature but depends on the knowledge and mindset of end users. A new big data cycle has been proposed in this paper, where the decision-maker is placed at the centre of the process. The proposed cycle is tested through two cases and, as a result of the suggested approach, two operations—filtering and framing—which are routinely carried out independently by scientists and end users in an unconscious manner, become clear and transparent. The result is a new cycle where four dimensions guide the interactions for creating value.