Strategic Resource Allocation for Sustainable Operation: A Machine Learning Approach for Chinese Enterprises Under Dual Economic Pressures
https://doi-xx0.org/6812/17671051479640 Shiying Zhang1,a, Hui Wang2,b, Yanhong Lu3,c, Haoxin Xiu4,d,* 1Business School of Nankai University, Tianjin, 300071, Tianjin, China; 2Institute of Economics, Shandong Academy of Social Sciences, Jinan, 250002, Shandong, China 3School of Economics and Management, Hebei University of Technology,Tianjin, 300401 ,Tianjin,China 4Qingdao Hospital, University of Health and Rehabilitation Sciences(Qingdao Municipal Hospital), Qingdao, 266011, Shandong, China aEmail: shiying.zhang@mail.nankai.edu.cn bEmail: ouc_wanghui@163.com cEmail: luyanhong2015@163.com dEmail: xhx19931011@163.com *Corresponding Author Abstract Amidst concurrent internal and external economic pressures, Chinese firms confront a critical challenge: the allocation of scarce resources. This study addresses this dilemma by introducing a leverage point framework. Utilizing firm-level data, our approach employs interpretable machine learning. The goal is to algorithmically discern the pivotal drivers of corporate sustainability within a severely resource-constrained environment. The empirical basis for our framework is a substantial dataset. It comprises 19,518 firm-year observations of Chinese A-share companies from 2010 to 2022. We deploy high-dimensional predictive models, specifically advanced ensemble algorithms like Random Forest and XGBoost. Sustainable operation is defined through two key metrics: the Sustainable Growth Rate (SGR) and Profit Volatility (PV). Following model construction, we examined feature importance and created Partial Dependence Plots (PDPs) to interpret the model, revealing key determinants and significant non-linear relationships. The results offer compelling insights. Financial Performance and Green Development emerged as the highest-ranking determinants of corporate sustainability overall. Our modeling also uncovered critical non-linear thresholds. An optimal range for R&D investment was identified between a ratio of 0.125 and 0.20. Similarly, a supplier concentration level around 20% was found to maximize sustainable performance. Furthermore, the analysis exposed a variety of significant interaction effects, notably between the ROE growth rate and the adoption of green management practices. This research significantly advances the understanding of corporate sustainability drivers. It presents a novel, interpretable, and data-driven framework for strategic resource allocation. This framework is particularly valuable for firms navigating competing demands during periods of economic strain. Ultimately, the study provides actionable, evidence-based guidance. This advice aims to ensure firms not only capture value generation but also cultivate the resilience necessary for long-term growth. Keywords: Financial Performance, Green Development, R&D Investment, Supply Chain Management, Machine Learning 1 Introduction Modern Chinese businesses are facing a collision of serious external and internal pressures that undermine their underlying operational viability and potential for long-term sustainability. Externally, geopolitical tensions have manifested as strategic technology embargoes related to access to important semiconductors, and imposition of tariffs on significant exports like new energy vehicles. Internally, the economy is dealing with the repercussions of a severe real estate slump. Collapsing builders, such as Evergrande Group, have created a domino effect for upstream construction suppliers in terms of capital flow, not to mention that downstream home buyers have properties that they likely will never get. This has tightened liquidity across the economy, with firms in every industry being forced to either reduce salaries or lay-off staff, exacerbating corporate instability in every sector and lack of resources. In this “economic winter”, the businesses are in a strategic conundrum. On the one hand, firms must urgently restrict costs and remain focused on core activities for short-term survival and, on the other, national policy requires firms to make major forward-looking investments. The government’s “dual carbon” targets are forcing firms to transition to green development and requiring significant capital expenditures for environmental protection and energy-saving initiatives. The ideological thrust for innovation-based growth from the strategic directions outlined in the 20th National Congress requires firms to commit substantial funds to R&D. These long-term implications are critical to developing national competitiveness and accountability for ecological well-being, yet firms may not have the financial means to support this longer-term focus.The most notable effect of this resource constraint is the broken capital chain and disrupted supply chain activities that make it extremely challenging for managers to finance green initiatives and R&D in addition to making sure they stay financially solvent and their supply chains resilient. Therefore, how to make the best use of severely constrained resources has become the biggest issue for corporate viability and sustainable development. Existing literature on resource constraints has some valuable suggestions to offer. For example, it is found that firms following a ‘balanced’ strategy, allocating constrained resources to functions like marketing and R&D, perform better than firms allocating in only one function [1]. Another body of literature suggests that resource constraints can paradoxically spur innovation because firms must think creatively and make good use of resource constraints [2]. However, much of this literature has significant limitations in light of today’s complicated crisis. First, the studies tend to lack multi-dimensional analyses examining the role of the constraints on a single domain, say innovation, without consideration of the fact that green development, supply chain management and financial health are all now connected. Second, the type of analytical models used are often linear models which examine how investments, say in R&D, affect financial performance without consideration of the reciprocal and synergetic nature of these domains [3]. Scholarly literature, in general, does not allow for a unified framework of the relational, and at times conflicting, relationships among these three investment areas. This is important when thinking about the complexities faced in today’s environment. In order to move forward in a way that rejects the path of standard linear analysis, this study takes a machine learning (ML) approach. We have selected reasoning for a computational perspective because ML will allow for modelling of the complex, non-linear and bidirectional relationships that comprise corporate investment systems. Standard econometric approaches encounter endogeneity and multi-collinearity among variables and therefore routinely cannot model the multilayered complex interactions. Management uses simultaneous ML algorithms to simulate and forecast outcomes. Rather than limiting the analytical approach to cause and effect analysis pursued in traditional economics, the focus of the current research uses a whole-portfolio effect of a combination of investments that create the necessary green development, innovation and supply chain mix to achieve a baseline financial objectives as well as enable sustainable operation. ML model, by estimating prior data, identifies baseline variables and index points that would