While the central government of China established its "dual carbon" goals, transformation of energy structure has become key component of the country's energy revolution and economic reform. Installment of the "Source-Grid-Load-Storage" (SGLS) systems is crucial for implementation of the ESG goals in domestic oilfields and uplifting of the economic efficiency for oil production. The SGLS system is a micro power grid capable of dispatching power among multiple equipment in the most efficient way but to consider multi factors including power generation, power storage, and electrical load. However, the randomness and intermittency of renewable energy power generation imposes significant challenges to the SGLS systems control, making it difficult to assess the economic benefits and therefore being undervalued by oil companies for its potential ESG benefits.To promote SGLS systems to be applied in the oilfields, this paper proposes a model to assess economic benefits of the SGLS system. Based on real data generated by sample Oilfield, an operation model designed for the SGLS system is established to access the optimal cost structure. Factors that have been built into the model include the main grid purchasing cost, dissipation cost during power transport, photovoltaic power generation cost, and energy storage cost. By calculating the optimal cost structure with the mentioned multi factors built in, the model can predict operational outcome of the SGLS system in real-time and guide on the power dispatching. Meanwhile, the model is trying to maintain the minimum requirement of energy reserve. Based on scenario testing, the economic benefits of SGLS system in oilfield production are assessed with an optimized cost approach, therefore a valuable reference for the oil industry. This research also conducted interviews with key people in the oil industry and proposes strategies to improve the economic benefits of SGLS systems in Oilfield based on takeaways from those interviews. This could be value-adding to accelerate construction of SGLS systems and its application in the oilfields. Meanwhile, the SGLS operational model designed in this paper as a power generation solution is innovative to the energy industry and with obvious economic benefits. Therefore, it could also be an enabler of the oil industry’s sustainable development in the long run.

Under the new generation of technological and industrial revolutions, digital economy enterprises are increasingly becoming major contributors to socio-economic development. Their scale effect and marginal cost effect are different from traditional enterprises, which also raises concern and discussion on whether digital economy enterprises can promote more equitable and sustainable development of society. The participation of digital economy enterprises in the common wealth is an important source of legitimacy for their development. This thesis investigates the mechanism of the impact of their common wealth inputs on corporate financial performance by using a sample of digital economy firms among Chinese listed companies as a case study. It is found that, overall, the mechanism of the effect of firms' common affluence model on their financial performance has a positive effect. The main source of this positive effect is the secondary distribution of the firm, i.e., the legitimacy of tax contributions. Other legitimacy such as employee and shareholder legitimacy are not significantly associated with financial performance, while social philanthropic input from tertiary distribution participation has a significant negative effect. In the association of redistribution on firm performance, there is a positive facilitating effect on firms' R&D efficiency and a negative moderating effect of economic policy uncertainty. It suggests that there are differences in the impact of firms' legitimacy initiatives, such as tax contributions, on performance under different firm development expectations. Whereas in the third distribution, firms' R&D efficiency has a crowding-out effect on the economic gains from the legitimacy of common wealth participation, economic policy uncertainty has a reinforcing effect in the third distribution of firms. The above suggests that the development of digital economy firms is more positively facilitated by official legitimacy and currently lacks the constraints of industrial ecology from internal and public scrutiny.

The rank aggregation problem has ubiquitous applications in operations research, artificial intelligence, computational social choice, and various other fields. Generally, rank aggregation is utilized whenever a set of judges (human or non-human) express their preferences over a set of items, and it is necessary to find a consensus ranking that best represents these preferences collectively. Many real-world instances of this problem involve a very large number of items, include ties, and/or contain partial information, which brings a challenge to decision-makers. This work makes several contributions to overcoming these challenges. Most attention on this problem has focused on an NP-hard distance-based variant known as Kemeny aggregation, for which solution approaches with provable guarantees that can handle difficult large-scale instances remain elusive. Firstly, this work introduces exact and approximate methodologies inspired by the social choice foundations of the problem, namely the Condorcet criterion, to decompose the problem. To deal with instances where exact partitioning does not yield many subsets, it proposes Approximate Condorcet Partitioning, which is a scalable solution technique capable of handling large-scale instances while providing provable guarantees. Secondly, this work delves into the rank aggregation problem under the generalized Kendall-tau distance, which contains Kemeny aggregation as a special case. This new problem provides a robust and highly-flexible framework for handling ties. First, it derives exact and heuristic solution methods for the generalized problem. Second, it introduces a novel social choice property that encloses existing variations of the Condorcet criterion as special cases. Thirdly, this work focuses on top-k list aggregation. Top-k lists are a special form of item orderings wherein out of n total items only a small number of them, k, are explicitly ordered. Top-k lists are being increasingly utilized in various fields including recommendation systems, information retrieval, and machine learning. This work introduces exact and inexact methods for consolidating a collection of heterogeneous top- lists. Furthermore, the strength of the proposed exact formulations is analyzed from a polyhedral point of view. Finally, this work identifies the top-100 U.S. universities by consolidating four prominent university rankings to assess the computational implications of this problem.