Purpose: The purpose of this study is to attempt to empirically examine the impact of disaggregate, eco-efficiency-based measures of corporate environmental performance (CEP) on corporate financial performance (CFP) of Indian companies. Further, recent theories contending a bidirectional causality between them is also explored.Design/methodology/approach: Secondary data of 224 Indian S&P 500 companies from 2002 to 2011 are used to run panel data regression models for examining the impact of CEP measures on accounting-based CFP measures.Findings: The empirical results are statistically significant and provide evidence for a positive association of eco-efficiency-based CEP metrics on CFP metrics, thereby supporting Porter's win–win hypothesis. Further, the results evidence a positive bi-directional causality between CEP and CFP for one period time lag signalling possibility of mutual reinforcement in CEP–CFP relationship.Research limitations/implications: The study has used data for the period 2002–2011 and eco-efficiency metrics – energy, water and material efficiencies due to availability.Practical implications: The results have implications to both corporate managers as well as policymakers across all industries for emphasizing on eco-efficiency-based (proactive) environmental sustainability initiatives to enhance both financial and environmental bottom lines.Originality/value: The study contributes to scarce empirical literature analysing the impact of CEP on financial performance. To the best of authors's knowledge, event studies, portfolio studies and perceptual data-based empirical studies exist in India. This study is unique in that it examines long run effect of eco-efficiency-based CEP metrics which is pertinent in a rapidly growing emerging market – India, where, eco-efficiency is considered quintessential for sustainable development.

With the electric power grid experiencing a rapid shift to the smart grid paradigm over a deregulated energy market, Internet of Things (IoT)-based solutions are gaining prominence, and innovative peer-to-peer (P2P) energy trading at a micro level is being deployed. Such advancement, however, leaves traditional security models vulnerable and paves the path for blockchain, a distributed ledger technology (DLT), with its decentralized, open, and transparency characteristics as a viable alternative. However, due to deregulation in energy trading markets, most of the prototype resilience regarding cybersecurity attack, performance and scalability of transaction broadcasting, and its direct impact on overall performances and attacks are required to be supported, which becomes a performance bottleneck with existing blockchain solutions such as Hyperledger, Ethereum, and so on. In this paper, we design a novel permissioned Corda framework for P2P energy trading peers that not only mitigates a new class of cyberattacks, i.e., delay trading (or discard), but also disseminates the transactions in a optimized propagation time, resulting in a fair transaction distribution. Sharing transactions in a permissioned R3 Corda blockchain framework is handled by the Advanced Message Queuing Protocol (AMQP) and transport layer security (TLS). The unique contribution of this paper lies in the use of an optimized CPU and JVM heap memory scenario analysis with P2P metric in addition to a far more realistic multihosted testbed for the performance analysis. The average latencies measured are 22 ms and 51 ms for sending and receiving messages. We compare the throughput by varying different types of flow such as energy request, request + pay, transfer, multiple notary, sender, receiver, and single notary. In the proposed framework, request is an energy asset that is based on payment state and contract in the P2P energy trading module, so in request flow, only one node with no notary appears on the vault of the node.Energy request + pay flow interaction deals with two nodes, such as producer and consumer, to deal with request and transfer of asset ownership with the help of a notary. Request + repeated pay flow request, on node A and repeatedly transfers a fraction of energy asset state to another node, B, through a notary.

Association rule mining (ARM) discovers correlations between different item sets in a transaction database. It provides important knowledge in business for decision makers. Association rule mining is an active data mining research area and most ARM algorithms cater to a centralized environment. Centralized data mining to discover useful patterns in distributed databases isn't always feasible because merging data sets from different sites incurs huge network communication costs. In this paper, an improved algorithm based on good performance level for data mining is being proposed. In local sites, it runs the application based on the improved LMatrix algorithm, which is used to calculate local support counts. Local Site also finds a center site to manage every message exchanged to obtain all globally frequent item sets. It also reduces the time of scan of partition database by using LMatrix which increases the performance of the algorithm. Therefore, the research is to develop a distributed algorithm for geographically distributed data sets that reduces communication costs, superior running efficiency, and stronger scalability than direct application of a sequential algorithm in distributed databases.